‘YEAH, I DOUBT IT.’ ‘NO, IT’S TRUE.’ HOW PARADOXICAL RESPONSES IMPACT THE COMMON GROUND
by
ERIN ALISA GUNTLY
A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
in
THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES
(Linguistics)
The University of British Columbia (Vancouver)
March 2021
© Erin Alisa Guntly, 2021 The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:
‘Yeah, I doubt it.’ ‘No, it’s true.’ How paradoxical responses impact the common ground submitted by Erin Alisa Guntly in partial ful llment of the requirements for
the degree of Doctor of Philosophy in Linguistics
Examining Committee:
Lisa Matthewson, Linguistics, UBC Supervisor
Rose-Marie Déchaine, Linguistics, UBC Supervisory Committee Member
Carla Hudson Kam, Linguistics, UBC Supervisory Committee Member
Marcin Morzycki, Linguistics, UBC University Examiner
Jessica de Villiers, English, UBC University Examiner
Manfred Kri a, Leibniz Center for General Linguistics, Humboldt University of Berlin External Examiner
ii Abstract
Speakers use paradoxical responses, such as ‘yeah, that’s not right’ and ‘no, I agree’, but the func- tion of these responses is not clear. While one use of response particles (RPs) such as ‘yeah’ and ‘no’ is to signal acceptance or rejection of at-issue content into the common ground, the linguistic content that follows these RPs in paradoxical responses suggests that ‘yeah’ is not agreeing to at- issue content, nor is ‘no’ rejecting it. This raises the question, what are these responses signalling to update the common ground? This dissertation argues that the two components of paradoxical responses, the RPs and the followup content, are selecting di ferent targets. The set of targets includes not only the at-issue content, but also speaker beliefs and the questions under discussion. Testing this hypothesis, called the Response Target Hypothesis, draws on a mix of methodologies, one experimental and one rooted in corpus data. Corpora of transcribed, unscripted conversations were searched for RPs plus followup con- tent; 200 tokens for ‘yeah’ and ‘no’ were identi ed for a total of 400, 173 of which were paradox- ical. In addition to accepting and rejecting the at-issue content, responses which targeted speaker belief and questions under discussion were also frequent. In paradoxical responses, the followup content almost exclusively targets the at-issue content, and RPs select the additional targets. The experiment presented two-phase scripted conversations to participants: one phase using a combination of response particle and followup content, and the second phase checking if the common ground was updated by phase one. Participants were asked to rate the acceptability of the conversation af er hearing both phases. Conversations which included paradoxical responses had a likelihood of being judged ‘appropriate’ or ‘very appropriate’ similar to those conversations which included non-paradoxical responses but which varied only in the RP. In other words ‘yeah, it wasn’t’ patterened along similar lines as ‘no, it wasn’t.’ Both data streams support the hypothesis that RPs can target more than at-issue content; the results demonstate that RPs that have followup content can target speaker beliefs and questions under discussion independent of at-issue content.
iii Lay Summary
Speakers use paradoxical responses such as ‘yeah, I don’t think so,’ and ‘no, you’re right,’ in con- versation with surprising frequency, but what do they mean? This dissertation shows how those responses can have three functions: They can accept or reject the truth of what was said (“That’s true.’’ or “That’s not true.’’); they can accept another speaker’s belief in what was said (“I know you think that, but it’s not true.’’); and they accept or reject the topic of the discussion (“We can’t know, so let’s not discuss it.’’ or “We already know, so let’s not discuss it.’’). The dissertation uses corpus and experimental data to show that paradoxical responses can sometimes be more appropriate than non-paradoxical responses and that the content af er ‘yeah’ and ‘no’ is more important in determining the function of the response.
iv Preface
This dissertation is the original, unpublished work of Erin Alisa Guntly. The experiment in Chapter 4 was conducted under UBC Behavioural Research Ethics Board Certi cate Number H16-03338.
v Contents
Abstract ...... iii
Lay Summary ...... iv
Preface ...... v
Contents ...... vi
List of Figures ...... xi
List of Tables ...... xiii
Acknowledgements ...... xv
Dedication ...... xvii
1 The research question and hypothesis ...... 1 1.1 Introduction to paradoxical responses ...... 1 1.2 Basic background and terminology ...... 4 1.2.1 Semantic terms ...... 4 1.2.2 Common ground ...... 6 1.2.3 Conversational components ...... 7 1.3 The Response Target Hypothesis and its core predictions ...... 12 1.4 Evidence to test the hypothesis ...... 16 1.5 Preview ...... 17
2 Tracking information through discourse ...... 18 2.1 Before an utterance ...... 19 2.1.1 Sources of Common Ground ...... 19
vi 2.1.2 Context of discourse ...... 23 2.1.3 Audience design and accomodation ...... 25 2.1.4 Modeling the state of information before the utterance ...... 26 2.2 The exchange, a two-step process ...... 27 2.2.1 The assertion ...... 29 2.2.1.1 The at-issue content ...... 29 2.2.1.2 The question under discussion (QUD) ...... 31 2.2.1.3 Statements of belief ...... 34 2.2.1.4 Modelling the utterance ...... 38 2.2.2 The response ...... 40 2.2.2.1 Response options ...... 43 2.2.2.2 The use of response particles ...... 44 RPs as anaphors and RP elipsis ...... 44 Response polarity ...... 48 2.2.2.3 Adopting a view of response particles ...... 50 2.2.2.4 Modeling the response ...... 51 2.3 Af er the exchange ...... 52 2.3.1 Updating the common ground ...... 52 2.3.2 Models of discourse ...... 56 2.3.2.1 The Table ...... 56 2.3.2.2 Commitment Spaces ...... 58 2.3.3 Role of memory ...... 61 2.3.4 Modelling common ground update ...... 62 2.4 Summary ...... 65
3 Naturally occuring data ...... 66 3.1 Methodology ...... 66 3.1.1 Selecting the data (corpus and “wild”) ...... 67 3.1.2 What was excluded from my study ...... 74 Fragmented discourse ...... 74 Missing context ...... 75 Broadcast genre conventions ...... 75 Direct questions ...... 76 Quizzes ...... 76 Backchanneling ...... 77
vii Other syntactic uses ...... 78 3.1.3 Distinguishing between agreeing and disagreeing content ...... 78 3.1.4 Determining the question under discussion ...... 83 3.1.5 Summarizing the methodology ...... 85 3.2 An overview of the corpus data ...... 86 3.3 Non-paradoxical responses targeting only at-issue content ...... 88 3.3.1 ‘Yeah’ responses accepting at-issue content ...... 89 3.3.1.1 Direct agreement ...... 89 3.3.1.2 Entailing agreement ...... 91 3.3.2 Agreeing with negative polarity ...... 97 3.3.3 ‘No’ responses rejecting at-issue content ...... 100 3.4 Accepting belief and rejecting at-issue content ...... 102 3.5 Targeting the question under discussion (QUD) ...... 105 3.5.1 Accepting the QUD ...... 106 3.5.2 Rejecting the QUD ...... 110 3.5.2.1 Answer to QUD already in common ground ...... 110 3.5.2.2 QUD will not bene t from further discussion ...... 112 3.6 Conclusions and Summary ...... 115
4 Experimental results ...... 117 4.1 Experiment ...... 117 4.2 Method ...... 119 4.2.1 Participants ...... 119 4.2.2 Stimuli design ...... 121 4.2.2.1 Exchange I: Establish the common ground ...... 123 Scalar versus nonscalar predicates ...... 123 Response particle and followup content responses ...... 124 4.2.2.2 Exchange II: Check the common ground ...... 125 4.2.3 Experimental Task ...... 126 4.2.4 Materials ...... 127 4.2.4.1 Visual aspects of the stimuli ...... 127 4.2.4.2 Audio aspects of the stimuli ...... 131 4.2.5 Procedure ...... 133 4.3 Results ...... 138 4.4 Discussion ...... 151
viii 4.4.1 How the experimental results support the hypothesis ...... 151 4.4.2 Potential variables beyond the study ...... 152 4.4.2.1 Intonation ...... 152 4.4.2.2 Listener status ...... 153 4.5 Summary ...... 154
5 Conclusions ...... 155 5.1 Overall conclusions ...... 155 5.1.1 Comparing the data to the predictions ...... 156 5.1.1.1 Accepting and rejecting the at-issue content ...... 156 5.1.1.2 Accepting belief while rejecting at-issue content ...... 157 5.1.1.3 Accepting and rejecting the question under discussion (QUD) 160 5.1.2 Overall conclusions from the data ...... 161 5.2 Research into ‘yeah no’ ...... 164 5.3 Opportunities for further research ...... 165 5.3.1 Intonational variation ...... 167 5.3.2 Responses and Information Structure ...... 168 5.3.3 Other languages ...... 169
Bibliography ...... 171
Appendices ...... 180
A Corpus response forms from Chapter 3 ...... 181
B Corpus Data ...... 184 B.1 COCA ‘yeah’ (92 examples) ...... 185 B.1.1 COCA ‘yeah’ + agree (55 examples) ...... 185 B.1.2 COCA ‘yeah’ + disagree (37 examples) ...... 204 B.1.2.1 COCA ‘yeah’ + disagree, accept belief (15 examples) . . . . . 204 B.1.2.2 COCA ‘yeah’ + disagree, accept QUD (22 examples) . . . . 210 B.2 LDC ‘yeah’ (93 examples) ...... 217 B.2.1 LDC ‘yeah’ + agree (67 examples) ...... 217 B.2.2 LDC ‘yeah’ + disagree (26 examples) ...... 233 B.2.2.1 LDC ‘yeah’ + disagree, accept belief (15 examples) ...... 233 B.2.2.2 LDC ‘yeah’ + disagree, accept QUD (11 examples) ...... 238
ix B.3 COCA ‘no’ (95 examples) ...... 240 B.3.1 COCA ‘no’ + agree (negative concord) (29 examples) ...... 240 B.3.2 COCA ‘no’ + disagree, rejecting the at-issue content (59 examples) . . . 249 B.3.3 COCA ‘no’ + agree, reject QUD (7 examples) ...... 270 B.4 LDC ‘no’ (98 examples) ...... 272 B.4.1 LDC ‘no’ + agree (negative concord) (37 examples) ...... 272 B.4.2 LDC ‘no’ + disagree, rejecting the at-issue content (33 examples) . . . . 279 B.4.3 LDC ‘no’ + agree, reject QUD (28 examples) ...... 286
C Experimental data ...... 293
x List of Figures
2.1 Gradient conception of common ground ...... 42 2.2 Non-gradient conception of common ground ...... 42 2.3 ‘Yes’ as isolated response to positive and negative questions under Kramer and Rawl- ins (2008) ...... 45 2.4 Tree of ‘yes’ response in (35) ...... 45 2.5 Updating the common ground (or not) during discourse ...... 53 2.6 Updating the common ground (or not) during discourse, with polarity ...... 54 2.7 Updating the common ground (or not) during discourse, with polarity and iteration 55 2.8 Sample Context Structure from Farkas & Bruce ...... 57 2.9 Sample Context Structure from Farkas & Bruce af er utterance is introduced . . . . 57 2.10 Diagrams of Commitment Spaces in (55), from Kri a (2015, p. 334) ...... 60 2.11 Updated common ground af er line 2 ...... 63 2.12 Updated common ground af er line 5 ...... 64
3.1 Flowchart for determining agreeing content ...... 85
4.1 A selection of slides from the stimuli ...... 128 4.1 A selection of slides from the stimuli (cont.) ...... 129 4.2 Reference code location on slide ...... 130 4.3 “Yeah, it was.” ...... 132 4.4 “Yeah, it wasn’t.” ...... 132 4.5 “No, it was.” ...... 132 4.6 “No, it wasn’t.” ...... 132 4.7 Sample training slide, labeled in lower right corner ...... 134 4.8 Illustration of slide presentation overview in Tab. 4.7 ...... 136 4.8 Illustration of slide presentation overview in Tab. 4.7 cont...... 137 4.8 Illustration of slide presentation overview in Tab. 4.7 cont...... 138
xi 4.9 Count of Ratings ...... 140 4.10 Stacked bar graph of acceptability ratings ...... 142 4.11 Interactions of followup content and yeah/noin convergeand diverge . . . . . 144 4.12 Interactions of response particle and agree/disagreein convergeand diverge 145 4.13 Interactions of three variables ...... 146 4.14 Stacked bar graph of probabilities ...... 147
5.1 Probabilities of appropriate ratings for paradoxical and nonparadoxical response stimuli ...... 163 5.2 Kri a’s Commitment Space Development (Kri a, 2015, p. 334) ...... 166 5.3 Intonation of ‘yeah it is’ from example ...... 168
xii List of Tables
1.1 Results of Google search of combinations ...... 3 1.2 Predicted appropriate responses of The Response Target Hypothesis ...... 15 1.3 Predictions of The Response Target Hypothesis ...... 15
2.1 Constraints on discourse contexts ...... 24 2.2 Polarity features of response particles ...... 48 2.3 Polar response particles in English and Romanian ...... 49
3.1 Percentage of included results from total search results (est.) ...... 70 3.2 Predicted appropriate responses of The Response Target Hypothesis ...... 86 3.3 Distribution of Corpus examples, n=400 ...... 86 3.4 Summary of corpus data and discussion sections ...... 88 3.5 Accepting at-issue contentwith ‘yeah’ + agree highlighted (Table 3.4 repeated) . . . 89 3.6 Accepting at-issue content with ‘no’ + agree highlighted (Table 3.4 repeated) . . . . 97 3.7 Rejecting at-issue content with ‘no’ + disagree highlighted (Table 3.4 repeated) . . . 100 3.8 Accepting belief and rejecting at-issue content with ‘yeah’ + disagree (Table 3.4 re- peated) ...... 103 3.9 Accepting the QUD with ‘yeah’ + disagree highlighted(Table 3.4 repeated) . . . . . 106 3.10 Reject QUD with ‘no’ + agree highlighted (Table 3.4 repeated) ...... 110 3.11 Distrubution of Corpus examples, n=400 (duplicate of Table 3.3) ...... 115 3.12 Summary of corpus examples ...... 116
4.1 Experimental Predictions of Response Target Hypothesis ...... 118 4.2 Notation for results discussion ...... 119 4.3 Overview of English native speaker participants’ primary home languages, age and gender ...... 120 4.4 Template of stimuli script ...... 122
xiii 4.5 Summary of example dialog in (1) ...... 123 4.6 Slide reference codes ...... 130 4.7 Slide presentation summary of Fig. 4.8, pages 136–138 ...... 135 4.8 Notation for results discussion (repeated from Table 4.2) ...... 138 4.9 Average and count of responses (n=324, 4 of each type per participant) ...... 139 4.10 P -values of four variables ...... 143 4.11 Probabilities of acceptability under experimental conditions ...... 147 4.12 Probabilities of converge, from Table 4.11 ...... 149 4.13 Overview of participant ratings, predictions and probabilities ...... 150 4.14 Polarity of responses ...... 152
5.1 Summary of Response Target Hypothesis predictions, experimental and corpus data 162
A.1 Responses to p—‘yeah’ ...... 182 A.2 Responses to p—‘no’ ...... 183
B.1 Corpus Data by outcome and corpus ...... 184 B.2 Examples in appendix ...... 185
C.1 Count of acceptability ratings under experimental conditions ...... 294
xiv Acknowledgements
I had my doubts that I would nish. That I would complete my dissertation became, in my mind, an increasingly remote possibility as things went on, but here I am. Finished. The most important person who made that happen is Lisa Matthewson, my supervisor. She saw what was good and interesting in my work even when I didn’t. Her honesty was at the heart of my mantra, Lisa says it’s OK so it must be OK, because she was all the things I needed in a supervisor: patient, exacting, honest, encouraging, and committed to my success. I’m so honored that she took me on, almost on the spur of the moment, and I’m so grateful for everything she’s done for me over the past nine years. I’m also happy that, af er all of our conversations and ups and downs over the years, we have been able to become friends. Thank you, Lisa, for encouraging me in more ways than you know. I really did have the perfect committee, because in addition to Lisa, I had Carla Hudson-Kam and Rose-Marie Déchaine, who were incredibly supportive both intellectually and personally. Carla’s guidance through the experiment and analysis made it possible to do that work and to get meaningful, signi cant results. Rose-Marie has a mind like no other, razor sharp and analytical, and even when I grumbled I knew that she was usually right. I’m sorry about the grumbling, Rose-Marie, but I’m grateful for your patience. Both Carla and Rose-Marie have made me a better scholar, and they’re the voices in my head that push me to be a better writer as well. My RAs were also instrumental in doing the experiment, Ivana Prpic, Brittany Mallet, Naima Mansuri and Bridget Patchett. They ran participants, proofread scripts, entered data, and did pretty much anything else that I asked. Ivana also made recordings and did the drawings for the stimuli, not to mention rating all of the corpus data. I could not have asked for a better group of people to help me in my research. I’m also part of a wonderfully supportive cohort. Hermann Keupdijo, Adriana Osa-Gomez del Campo, Emily Sadlier-Brown, and honorary cohort members Oksana Tkachman and Neda Todorovic, I don’t know what I can say, but I don’t think I need to say anything. You already know. There’s something about going through this experience together that creates a unique
xv kind of connection. I’m glad to have shared this time with you. Natalie Weber also understands all of the emotions that no one tells you about when you get onto the PhD roller coaster, and we’ve spent a lot of time re ecting and commisserating. Ella Fund-Rezniczek has also been a reliable sounding board and co fee companion. Thank you to both! I must thank my oldest and dearest friend, Natalie Piispanen, who has of en listened patiently to me being unreasonable, and then kindly told me just how unreasonable I was being. She’snever expressed even the slightest doubt that I would nish and that everything would work out; she just doesn’t entertain that possibility. It’s very reassuring. My sister Kori has supported me, making recordings, reading through passages, serving as my consultant for judgements. She also helps me nd down time, because sometimes it’s important to put the computer away for an hour. Finally, and most importantly, I have to recognize my parents. My dad passed away quite suddenly in May of 2019, and I am still heartbroken that he’s not here for this. He was always one of my biggest supporters and best sounding boards, and he was so excited to hear about my work. I’ve dedicated the dissertation to him, and I included his artwork in the defense presentation, because he’s so much a part of this experience, even though he’s gone. His passing makes my nishing bittersweet; I wish he were here to share this time. I miss him so much, but he’s always with me. And my mom. I don’t think it’s possible to put into words how much she’s done for me, and how grateful I am for her unquestioning support. My mom has always been a bit of a mir- acle worker; she just makes things work because they can’t not work. Even when my dad passed away, she still just made things work for me, no matter what I needed. These words are a poor expression of the depth of my gratitude, but thank you. Thank you to all the people who have been there for all the ups and downs. I will always be grateful.
xvi Dedication
To my dad, who always with me
In memory of Donald Allen Guntly, 1946-2019
Show me that river, take me across Wash all my troubles away Like that lucky old sun, give me nothing to do But roll around heaven all day
That Lucky Old Sun by Ray Charles
xvii Chapter 1
The research question and hypothesis
1.1 Introduction to paradoxical responses
As users of English, we take two things for granted: that we talk with each other in order to “es- tablish a joint understanding” (Garrod and Pickering, 2004, p 8), and that people within the same conversation are understanding, or at least trying to understand, that conversation in roughly the same way (Clark and Brennan, 1991; Grice, 1975, a.o.). In order to achieve that goal of understand- ing, speakers of a given language respond to each other over the course of a conversation. The understanding established during discourse can be personal (a story about last weekend), professional (a job o fer), informative (a report about a bus detour), re ective (talking through an emotion), humorous (an embarrasing anecdote), or re ect some other purpose, but a rea- sonable assumption is that the point of most conversations, however brief, is for at least one person to convey a message that at least one other person might understand and accept; in other words, the point is to expand the common ground between the participants in the conversation.1 If, for example, a conversation was intended to agree on a meeting time, then it would proba- bly not be considered a successful conversation if one person walked away thinking the meeting would be Friday and the other person thought Thursday. To help ensure an understanding, En- glish speakers are likely to use words like ‘yeah’ and ‘no’ to manage this exchange by signalling (dis)agreement. Response particles like ‘yeah’ and ‘no’ might be thought to signal agreement and disagree- ment with the preceding utterance, respectively. One of the goals of this dissertation is to show that the pre-theoretic notion of ‘yeah’ and ‘no’ meaning ’agree’ and ‘disagree’ needs to be un- 1Other models of conversation do exist, notably ones that focus on origo ground (Déchaine et al., 2017), faultless disagreement (Umbach, 2014), or the interpersonal and social/interactional nature of discourse (Turnbull, 2003).
1 packed and examined more closely. Responses using those particles and phrases such as ‘I agree’ or ‘That’s not right’ con rm whether the speakers understood the information they have heard. Are ‘yeah’ and ‘no’ relegated only to con rming or denying understanding? What happens when the response particle and the following phrase seem to disagree? Consider the following exchange from an unscripted portion of a television show2:
(1) Context: Three friends and colleagu were trying to leave a beach but got lost and ended up back where they started. The one who w navigating said that he did not realize there were two se in the country, to which h colleagu replied angrily that there weren’t; the speakers have a history of teasing and insulting each other, they continue their conversation and use the similarity of “sea” to the letter c to su est an insulting description for JC. (RH says [si:] in both instanc .) RH: There’s two “c”s here [gesturing at himself and JM] for believing that that “c” [indi- cating JC] could navigate! JM: no you’re right! [looking at RH]
Is it a valid assumption that all of the participants have access to the same understanding at the end of the exchange? This is unclear from this small exerpt, but it is possible, even likely, that JC disagrees at least with the presupposition that he, JC, is a “c,” although he should at least be aware of RH’s view, even if he does not share it. Even under a generous interpretation, it is not possible to assume that ‘no’ simply rejects RH’s assertion, because ‘no’, in a naive understanding, does not get followed with “you’re right.” Using ‘no’ to deny the accuracy of a statement, and then following that ‘no’ with “you’re right” to con rm the accuracy of the same statement creates a paradox. So what could the combination of ‘no’ and ‘you’re right’ mean? Af er an exchange like that in (1), what is the state of the common ground between these three speakers? These are essentially the questions behind this dissertation:
(2) a. Is the common assumption about the ‘yeah’ signalling agreement and ‘no’ disagree- ment rejection valid? b. What do paradoxical responses, such as ‘no, you’re right,’ signal? c. What is the state of the common ground af er such a response?
2This exchange comes from The Grand Tour, produced by Amazon.
2 Table 1.1: Results of Google search of combinations
yeah [+] no [−]
you’re right [+] 618,000 655,000 it’s true [+] 588,000 648,000 you’re wrong [−] 442,000 494,000 it’s not true [−] 1.98m 517,000
Paradoxical responses are not isolated to the particular form of ‘no, you’re right,’ and they occur more frequently than one might assume. That ‘yeah’ and ‘no’ can both combine with ‘you’re right/it’s true’ and ‘you’re wrong/it’s not true’ is evident from a basic Google search, the results of which are shown in Table 1.1.3 It is unclear how these paradoxical responses impact the common ground, which is the information shared between speakers. For purposes of framing the research question, let us think of ‘yeah’ and ‘you’re right/it’strue’ as having a plus [+] value, and ‘no’ and “you’re wrong/it’s not true” as having a minus value [−] as relates to the truth of the preceding statement. Initial assumptions about how ‘yeah’ and ‘no’ work would predict that the combinations would pair like with like, so that ‘yeah [+], it’s true [+]’ would be more common than responses that combine di ferent polarities, such as ‘no [−], it’s true [+]’. But the Google results in Table 1.1 show exactly the opposite. The results show that, not only are divergent polarity responses not an anomoly, they are in fact the three most frequent responses: ‘yeah [+] it’s not true [−]’ (1.98m), ‘no [−] you’re right [+]’ (655,000), and ‘no [−] it’s true’ [+] (648,000). Google searching a phrase hardly gives the most indicative data about language usage, but these results force the question:when speakers use these paradoxical combinations, when JM says “no you’re right” in (1), what is being communicated? If we assume that “you’re right” is signaling agreement with RH’s statement, what is ‘no’ responding to? The question of paradoxical responses has received surprisingly little attention in the liter- ature. The work that has been done has focused on the Australian use of ‘yeah nah’ (Burridge and Florey, 2002) or has been more informal discussion that methodical research (Liberman, 2008b). Although researchers are beginning to turn their attention to the problem of paradoxi- cal responses (Tian and Ginzburg, 2016), no analysis has yet been o fered. The insights of these 3The Google search was conducted with all eight combinations on April 21, 2017. The terms were combined in all eight permutations, so that ‘yeah you’re right’ returned 618,000 results, ‘no it’s not true’ 517,000, etc.
3 threads of research, while interesting and informative, are not central to informing this disserta- tion; they are discussed more fully in Ch. 5. Understanding the possible targets of response particles requires that we understand how both the particles and their targets interact with the common ground. The common ground is the repository for information which is mutually agreed upon by discourse participants and which is available for both to use over the course of the conversation (Clark and Brennan, 1991; Stalnaker, 1978; Keysar, 1997). The data suggest that ‘yeah’ and ‘no’ may respond to things other than at-issue content, including speaker beliefs and the Question Under Discussion (QUD). By separating these components of discourse, we can attempt to identify what ‘yeah’ and ‘no’ are responding to in a more demonstrable fashion.
1.2 Basic background and terminology
The purpose of this section is to provide the background and terminology necessary to discuss the hypothesis and its predictions. It is divided into three sections, which focus on general semantic terms, terms relating to the common ground, and terms from conversation analysis.
1.2.1 Semantic terms
When considering the role of response particles, the most crucial semantic term to de ne is propo- sition, which is the semantic content of a full sentence and represents a function from worlds to truth values. A proposition is truth-conditional and of the type
(3) Utterance: Oh my god, those Seahawks fans are so freakin’ obnoxious! prop.: at-issue content. Fans of the Seahawks are obnoxious QUD. Are Seahawks fans obnoxious?
4 prop.: presupposition. There are fans of the Seahawks. prop.: speaker belief. Speaker X believes that Seahawks fans are obnoxious.
The utterance in (3) introduces several propositions. The at-issue content (sometimes ab- breviated AIC) is of en regarded as the point of the utterance. It is frequently, though not nec- essarily, new information that the speakers have not signalled their agreement on. Tonhauser (2012) identi es three properties which are critical in identifying at-issue content:
(4) Evidence for at-issue content a. direct agreement/dissent is possible b. it addresses the question under discussion (QUD) c. it determines the set of alternatives
Item (4.b) indicates that the at-issue content relates strongly to the Question Under Dis- cussion. The Question Under Discussion (QUD) can be thought of as the question which the discourse is intended to answer.4 An utterance may introduce more than one QUD, as Sec. 2.2.1.2 discusses, but the QUD identi ed in (3) is the narrowest of these QUDs. It introduces two propositions, namely p and ¬p which answer this narrow QUD. Presuppositions are propo- sitions which are assumed to already be in the common ground; presupositions are regarded as true, regardless of the truth the at-issue content. In (3), the utterance presupposed the existence of Seahawks fans, which is a necessarily true if the utterance itself is true. Speaker belief or com- mitment is the publicly declared belief of a given speaker at a given moment in the discourse. The speaker in (3) believes in their statement concerning Seahawks fans, but their interlocutor may not share this belief. The central question of this dissertation is which of these components ‘yeah’ and ‘no’ can target. To say that response particles can only respond to a proposition does not resolve the para- dox of why speakers can say “yeah no that’s right,” as one example. If the question is rephrased to ask about what kind of proposition each targets, then that opens up the possibility of resolving the paradox. An important note about terminology concerns concerns how the term ‘proposition’ is used. This dissertation builds on the work of several researchers from several disciplines, and most of the work discussed in following chapters uses the term ‘proposition’ to discuss what enters the 4More speci cally, the QUD is a set of answers to a question that arises from the discourse; more details on this in Sec. 2.2.1.2.
5 common ground. In order to fairly and accurately represent the work of others, I will not pre- sume to rede ne anyone’s use of ‘proposition’, instead I will simply use the term ‘proposition’ to present their work as faithfully as possible. However, my analysis relies on a consideration of types of propositions, in which the di ferent types of propositions in (3) each impact the com- mon ground in a way that is distinct from the other types of propositions. I will use terms such as those in (3) to refer to these types in my analysis. The data presented to this point suggest that it is not enough to simply say that the common ground contains “propositions,” because all of the exchanges resort to something beyond what was said. Without understanding the potential candidates for inclusion in the common ground, it will be impossible to understand what the common ground might therefore contain.
1.2.2 Common ground
A working de nition of common ground5 is this: the repository of information that the inter- locutors have signalled is mutually shared,6 and crucially, that is available for reference at a later point in the discourse. This de nition draws from observations that certain information must be understood to be shared by the interlocutors (Stalnaker, 1973, 1978), combined with observations that the information that is placed in the common ground is managed by the interlocutors over the course of the discourse through the minimal required e fort on the part of the interlocutors (Clark and Marshall, 1981; Clark and Wilkes-Gibbs, 1986; Clark and Brennan, 1991; Clark, 1996). That the information is available for reference at some later point in the conversation distin- guishes the common ground as a repository of information from the short term processing mem- ory that stores what was said verbatim (Sachs, 1967). This is an important distinction, because people are capable of recalling an utterance verbatim for a short time af er hearing it (Gurevitch et al., 2010), but the syntactic structure of an utterance is generally not persistent beyond a few seconds whereas the gist, the semantic content, persists for longer (Anderson, 1974; Sachs, 1967). Moreover, short term memory does not take into consideration the information that might be available to the other discourse participant(s). Keysar (1997) points out that the common ground is essentially meta-knowledge; “it is knowledge about knowledge” (Keysar, 1997, p. 2). The notion that information that was exchanged linguistically should be available for later reference nds support in experimental (Keysar, 1997; Keysar et al., 1998, a.o.) and theoretical 5A fuller discussion of the de nition of common ground is in Sec. 2.1.1. 6It is fair to ask how speakers signal that information is mutually shared, and a detailed answer will be provided in Sec. 2.3.1. For the following discussion, is should be su cient to recognize that speakers have several strategies, including the use of responses and response particles (Stalnaker, 1978; Clark and Brennan, 1991).
6 (Heim, 1982, a.o.) work on nominal referents.7 Consider the example in (5), in which a referent description is used in line 1, and pronouns in lines 2 and 3.
(5) Example of using the common ground for discourse referents 1. α: The guy who delivered my lunch to my o ce today was such an unbelievable jerk! 2. β: That’s awful. You should complain about him to the delivery company. 3. α: I didn’t get his name, or I would!
The only way that both speakers can know who “him/his” is in lines 2 and 3 is from the establishment of a referent in line 1 with the de nite description “the guy who delivered my lunch to my o ce today.” The only source of this information about the discourse referent for β is α’s linguistic description, which was su cient to place the referent in the common ground during the discourse, as both speakers then use the pronouns ‘him’ and ‘his’ to refer to him. If these two speakers continue their discussion, they can refer back to the surly delivery guy later in their discourse as well, because the referent remains available in the common ground.
1.2.3 Conversational components
Natural discourse is messy, containing false starts, self-corrections, turn management utterances, backchanneling and sentence fragments, none of which convey semantically full propositions. The motivations behind these non-sentential discourse components may be pragmatic (Trau- gott, 1982; Scheglo f, 1992; Sydorenko et al., 2014) or concerned with managing the process of discourse, such as turn taking (Scheglo f, 1982; Fox Tree and Schrock, 1999). Though these parts of discourse are crucial to the functioning of conversations, they are not the focus here. Rather, the focus is how response particles and their followup content are used to manage the linguistic content of the common ground. The cooperative principle (Grice, 1975), that speakers in conversation attempt to cooperate with each other, is important in expanding and using the common ground, in part because it dictates that speakers shape their message to be relevant to the discourse. Cooperation also rec- ognizes that speakers use knowledge beyond the discourse to shape what they say and how they say it. Clark and Wilkes-Gibbs (1986) look at cooperation between speakers and term this coop- eration the least collaborative effort (LCE). It is the minimal amount of work that speakers must do to communicate their message e fectively. Natural discourse is replete with examples of 7See Chapter 2 for a detailed discussion of this evidence.
7 LCE, as speakers frequently use utterances smaller than a clause, and interlocutors of en accept the contribution of these utterances by ‘ lling in the blanks,’ as in (6).
(6) A: That tree has— uh— uh— B: Tentworms. A: Yeah. B: Yeah. Clark and Brennan (1991, p. 226)
The LCE is the sum of the e fort of two (or more) speakers in conversation. Looking at (6), we can consider the alternatives to B completing A’s sentence. Had B not intervened, the e fort required of A to retrieve the word would have been much higher, and the e fort required of B to wait patiently would possibly also have been higher.
…speakers of en realize that it will take more collaborative e fort to design a proper utterance than to design an improper utterance and enlist their adresssees’ help. Speakers, for example, can present a provisional utterance and add try mark- ers8 to ask for con rmation. They can present a di cult utterance in installments and check for undertsanding af er each installment … They can invite addressees to complete an utterance they are having trouble with. And they have many other col- laborative techniques at their disposal. The principle of least collaborative e fort is essential for a full account of face to face conversation. Clark and Brennan (1991, p. 226)
LCE accounts for facets of discourse that are necessarily cooperative, such as understanding an implicit request, providing a missing word (as in (6)), and managing the common ground. Shober and Brennan (2008) also point out that LCE and its bene ts, namely reduced processing costs, are con ned to discourse because of its interactive nature. LCE will be particularly im- portant in Chapter 3, which will work through many examples of natural language taken from corpora, but its importance may not be obvious because speakers seem to take collaboration for granted, which becomes clear in cases such as (7).
(7) 1. A: uh about thirteen years ago uh we looked for a home and the things that we were seeing were uh not up to my standards
8Try markers are utterances such as “‘uh–” in (6) which signal to the interlocutor that the speaker is attempting to complete the utterance or inviting the interlocutor to intervene, although without necessarily surrendering their turn.
8 you know the very little insulation in the walls and very little insulation in the ceiling so 2. B: yes um-hum that doesn’t work these days 3. A: yeah and it was uh it wasn’t what we wanted so we did some research and i knew the land there’s a lot of ledge up here so you have to be careful Linguistic Data Consortium
The LCE is important in this conversation because it allows the speakers to arrive at what appears to be a mutual understanding of what “that” means in line 2. Line 1 contains several possible candidates, marked in bold face, but the one that the speakers select (according to my own judgements) is the one suggested by the clause in small caps: the building of homes like they have for 150 years. A straightforward test of this claim is to substitute the potential referents in the place of “that” and see which ones still make sense; (8) presents a few of these tests.9
(8) Testing antecedents for “that” in (7), line 2 1. # yes um-hum a home doesn’t work these days. 2. # yes um-hum [that] the things that we were seeing were not uh up to my standards doesn’t work these days. 3. yes um-hum the building of homes back then like they have for a hundred and fifty years doesn’t work these days.
The nal candidate, “the very little insulation in the walls [and] very little insulation in the ceiling,” are examples of how “they were still building homes back then…” Crucially, the absense of any other negotiation for meaning, plus the absence of any evidence of misunderstanding or disagreement, suggests that the discourse is in fact collaborative, but absent any overt discussion of what “that” means, the collaboration is also minimal. The purpose of the LCE is, af er all, to facilitate discourse by allowing the interlocutors to ‘ ll in the blanks’ of what is not explicit. Response particles are a category of words that are used to respond to utterances; response particles do not usually have their own substantive meaning but are instead used to manage dis- course between interlocutors by signalling continued attention, turn management, acceptance and rejection, etc. The set of English response particles includes ‘yes’, ‘yup’, ‘yeah’ and others which might loosely be referred to as ‘positive particles,’ and ‘no’, ‘nope’, ‘nah’, and similar as ‘negative particles.’ While particles within the positive and negative categories are frequently 9The complementizer [that] in brackets has been added where necessary to ensure grammaticality.
9 treated as variants of each other, their distribution and restrictions suggest that they are not in fact identical (Pope, 1972; Wiltschko, 2016), and this work will con ne itself to looking at ‘yeah’ and ‘no’. One reason for limiting the discussion is that ‘yeah’ and ‘no’ can appear in paradoxical responses, whereas other candidates, such as ‘yes’ and ‘nope’ cannot.
(9) 1. Yeah, it isn’t. 2. # Yes, it isn’t. 3. No, it is. 4. # Nope, it is.
The term response particl is preferred among the disciplines of discourse analysis and con- versation analysis, which are concerned primarily with how language is used in communication, while theoretical disciplines that are concerned with sentence level phenomena, such as syntax, generally use the term polarity particl . As this dissertation is concerned with the discourse level, the term response particles will be used. Where it is important to distinguish between response particles that have polarity (‘yeah’ and ‘no’) from other response particles (uh–, hmm), the term polar response particles, or prps, will be used. Several other terms are essential in discussing discourse organization, starting with the broad- est term, utterance. An utterance is simply what a speaker says; it can be a full sentence, or even a series of sentences, for instance as part of a lecture. Importantly, an utterance is doing some kind of work, and is usually categorized according to the type of that work: question, announcement, rejection, agreement, among many other types (Scheglo f, 2007). The notion of utterance ties closely with that of the speech act, which is in essence that which an utterance is accomplishing beyond its compositional meaning (Searle, 1969). For instance, an o ciant at a wedding is not simply saying the words “I now pronounce you…”, they are signalling the commencement of a legal union. This illustrates why my analysis rests less on the form of an utterance and more on what an utterance does in context (Pridham, 2001). On the other hand, an utterance can be a single response particle or something indicating backchanneling, such as uh-huh or mm as used for signalling continued attention (Scheglo f, 1982) and at least tacit acceptance of the preceding discourse (Tolinsand Fox Tree, 2015). Backchan- neling does not place information into the common ground, however, as there is no evidence of mutual acceptance, only evidence of cooperation in letting a speaker nish their turn; an inter- locutor can dispute the veracity of a previous claim in their turn. As backchanneling does not signal the beginning of a turn, it will have no additional content af er the backchannel itself; this is true when ‘yeah’ is used as a backchannel as well, so that the responses of ‘yeah’ plus followup content cannot be considered backchannels.
10 Turns are the most basic unit of discourse, and a turn is simply one speaker’s opportunity to speak with the expectation that the other discourse participants will give that speaker their attention. Turns can contain non-verbal content; when a speaker pauses to gather their thoughts but does not invite another speaker to contribute to the discourse, they are maintaining their turn. Turns contain utterances, but turns and utterances are not interchangeable. To illustrate the di ference, let us consider the hypothetical example in (10), noted with the conventions of discourse analysis (Tannen et al., 2015).
(10) An example of an interview 1. A. How did – how did you get into this work? 2. B. (laughter) Well that’s you know it’s quite a story but I just got to feeling [3 sec pause] it’s funny how s[ometimes… 3. A. [yeah 4. B. like you just get to the point in your life where you just have to you know change things. and I knew this woman who…
This example includes four utterances, but only two turns. The rst turn is Speaker A’sopen- ing question, and the second turn begins when Speaker B starts to respond. Speaker B takes a 3 second pause in Line 2 without ending their turn; Speaker A o fers a backchannel ‘yeah’ that overlaps most of Speaker B’s word ‘sometimes,’ but crucially Speaker B does not yield the oor to Speaker A, nor does Speaker A make any e fort to begin their turn. Speaker A’s ‘yeah’ is not followed with any meaningful content. Turns are also frequently organized into adjacency pairs (Scheglo f, 1982), where one turn relates directly to the previous turn. A common type of adjacency pair is question and answer, as in (11), which contains two adjacency pairs. It is a fairly common kind of exchange, that of order- ing at a co fee shop. It relies on the conventional nature of the exchange, in which the questions and responses are quite predictable for both parties. This particular example was overheard at a local Starbucks.
(11) 1 Barista: What can we get you? 2 Customer: Yeah, I’d like a tall dark roast with room and a bagel. 3 Barista: Toasted with cream cheese? 4 Customer: Yeah, that’d be great.
11 Assuming that the barista and customer have little if any previous interaction, the common ground that exists before the exchange in (11) is entirely derived from the shared environment and the shared familiarity of how ordering at a co fee shop works. This exchange contains two adjacency pairs, 1 and 2, and then 3 and 4, and these in turn represent four turns as well as four utterances. Now let us consider another exchange, this time between two friends:
(12) 1 N: So I got an email from Amy 2 E: yeah 3 N: that I’m really excited about, in part 4 E: yeah 5 N: because it completely circumvents my boss…
This exchange consists of ve utterances but only one turn; two of the utterances are backchan- neling, namely Speaker E’s contributions in lines 2 and 4. Backchanneling allows a listener to signal their continued attention to what a speaker is saying. Crucially, backchanneling does not signal agreement with what a speaker is saying, as the listener can still challenge the truth of what was said when their turn arrives. Backchanneling is also not a turn, because the speaker who is backchanneling, Speaker E in the case of (12), is not claiming the oor to make a contribution to the discourse, they are signalling their attention to Speaker N’s continued contribution; it is Speaker N’s turn still. In the discourse following (12), Speaker E could use their turn to reject part or all of what N said, perhaps by questioning who Amy is or by asserting that N doesn’t seem excited. The fact that E said ‘yeah’ here is not evidence of E’s acceptance of N’s claims. In both (11) and (12), ‘yeah’ is used to respond to the other speaker, but these uses are not the object of inquiry of this dissertation. In (11) line 4, ‘yeah’ is used to respond to what is understood as a yes/no question (even though it takes the form of a sentence fragment), and such a use of a prp should not be surprising. Example (12) demonstrates the use of ‘yeah’ as a backchannel.
1.3 The Response Target Hypothesis and its core predictions
In exploring the question of what can be placed in the common ground through the use of re- sponse particles, this dissertation proposes the hypothesis in (13), that the at-issue content and other propositions introduced by an utterance may be entered into the common ground, all of which are managed through the use of response particles. This dissertation asserts that response particles can target at-issue content, belief states, and the Question Under Discussion (QUD). The Response Target Hypothesis predicts ve possible outcomes:
12 (13) Hypothesis: Response particles, and the linguistic content following response particles as part of a response, can target (accept or reject) at least the following types of con- tent for inclusion into the common ground: at-issue content, speaker beliefs, the Question Under Discussion. Predicted targets and outcomes of responses and followup content: At-issue content 1. β accepts the at-issue content of α’s assertion; this puts the at-issue content in the common ground as something that both speakers agree is true. 2. β rejects the at-issue content in α’s assertion; this excludes the at-issue content from the common ground and forces a crisis (in the terms of Farkas and Bruce, 2010), the resolution of which falls to other assertions and responses. Speaker belief in at-issue content 3. β accepts α’s belief in the at-issue content without committing themselves to the same belief; this signals a mutually shared agreement concerning α’s be- lief in the at-issue content and the acceptance of that belief into the common ground, and crucially does not necessarily put the at-issue content into the com- mon ground. * Rejecting speaker belief is not considered felicitous in most contexts. Question Under Discussion (QUD) 4. β’s response accepts the QUD as a valid question to discuss, to which an answer is not currently available in the common ground or in β’s contribution. 5. β’s response rejects the QUD because the answer is already established in the common ground or the answer is unattainable at that moment.
The central claim of the Response Target Hypothesis is that response particles, and the lin- guistic content that follows them in responses, participate in the management of common ground by targeting di ferent types of propositions. The response can signal the acceptance (or rejection) of speaker beliefs (an idea which draws on Kri a (2015)), as well as the inclusion or exclusion of the at-issue content of a triggering utterance. The response particles can also target the QUD based on the information already con rmed in the common ground.
13 Rejecting a speaker’s belief is a logical posibility, but as a conversational move, it is all but impossible to deny another person’s belief. In researching this dissertation, I did not come across such an example, in corpora or anywhere else. The closest I have observed are parents of young children saying things like, ‘No, you don’t want a cookie.’ However, these crucially rely on follow- up content which makes explicit that the belief is being challenged. Notice, moreover, that the at- issue content of the child’s utterance (‘I want a cookie’) is also being challenged here. It is di cult to imagine that that the response particle ‘no’ could signal rejection of the previous speaker’s belief, without also signalling rejection of the at-issue content. The response particles ‘yeah’ and ‘no’ can target di ferent types of propositions, including at-issue content, beliefs and QUD, but there is no a priori assumption that ‘yeah’ always signals acceptance of the at-issue content into the common ground and ‘no’ always signals rejection. To be clear, there is no claim that ‘yeah’ and ‘no’ are radically di ferent from how they are currently understood; ‘yeah’ is still a positive response that generally signals agreement, and ‘no’ is still a negative response that generally signals disagreement. Rather this dissertation allows the possi- bility that ‘yeah’ may be included in a response which ultimately reject the at-issue content from the common ground, and ‘no’ may be present in a response which accepts the at-issue content into the common ground, on the basis of polarity misalignment (Examples of both are in Ch. 3). To see the Response Target Hypothesis applied to utterances, let us begin by looking at an adjacency pair structure. This template will serve as the basis for the data in later chapters.
(14) Adjacency pair template α:. Utterance contains a proposition consisting of at-issue content and representing α’s belief and discourse commitment, and responding to an understood QUD β:. response particle ‘yeah’ or ‘no’, and followup content that agrees or disagrees with the at-issue content contained in α’s utterance
There are four logical combinations of ‘yeah’ and ‘no’ response particles and agreeing and disagreeing followup content, and these combinations pattern with the rst four predictions of the hypothesis, given in Table 1.2. It is important to note that this table is not claiming that, for example, ‘yeah’ plus disagreeing content will always and only be consistent with Prediction 2, ac- cepting belief while rejecting at-issue content. It also does not claim the opposite, that Prediction 2 will always and only be triggered by ‘yeah’ plus disagreeing content. Rather the table represents the four logical combinations of particles and followup content and how those combinations are most likely to be interpreted.
14 Table 1.2: Predicted appropriate responses of The Response Target Hypothesis
Outcome ‘yeah’ + ‘no’ + agree ‘yeah’ + ‘no’ + agree disagree disagree
1 Accept AIC 2 Reject AIC 3 Accept Bel, Reject AIC 4 Accept QUD 5 Reject QUD
Table 1.3: Predictions of The Response Target Hypothesis
Response Impact on Common Ground
1. Accept AIC ‘yeah’ + agree at-issue content entered into the common ground 2. Reject AIC ‘no’ + disagree common ground is not updated 3. Accept Bel, Reject ‘yeah’ + disagree1 belief, not at-issue content, entered into AIC common ground 4. Accept QUD ‘yeah’ + disagree2 common ground not updated 5. Reject QUD ‘no’ + agree at-issue content already in common ground, therefore common ground not updated
1 In order to accept belief and reject at-issue content, the disagreement must reject the at-issue content, for example with a response of ‘that’s not true’.
2 In order to accept the QUD without accepting the at-issue content into the common ground, the disagreement must be compatible with the at-issue content while not signalling acceptance, for example, ‘that’s an interesting idea’. See Sec. 3.1.3 for a more detailed discussion.
Table 1.2 maps the theoretical prediction to the discourse response. The checkmarks ( ) in- dicate that a given response, such as ‘yeah’ + agree, is predicted to be appropriate because it signals the corresponding outcome; the ballot cross () indicate that those combinations of responses and interpretations are not predicted. Table 1.3 shows how the responses in Table 1.2 are predicted to update the common ground.
15 In the Response Target Hypothesis, the common ground is not limited to including only at-issue content, which is Outcome 1, or restricting only at-issue content, Outcome 2. Beliefs can also be placed into the common ground, as in Outcome 3. In this case, the rules concerning mutual agreement still hold, in that while the at-issue content is not mutually held to be true and therefore not in the common ground, α’s belief in the truth of the at-issue content is mutually accepted by α and β, and α’sbelief can therefore be placed in the common ground and be available for reference at a later point. Outcome 4 can update the common ground to include a QUD for which there is no currently available answer, and Outcome 5 can reject a QUD because there is already an answer. Outcome 5 does not result in an update to the common ground, even though it signals agree- ment. This is because the response is targeting something that is neither the belief nor the at- issue content. Example (1) might be understood as an example of this outcome, wherein JM’s response of “no, you’re right!” signals such strong agreement with the at-issue content, namely that “there’s two [idiots] here...”, that ‘no’ dismisses the question under discussion as irrelevant, likely because it has already been established in the common ground. The analysis focuses on what is syntactically and semantically present and only discusses per- sonal or non-linguistic motivations when they are clearly present, for example if a transcript notes laughter. However, as a native North American English speaker with a similar cultural back- ground as the speakers and participants in this dissertation, I also use widely available cultural knowledge of the context of a discourse to support my analysis. This dissertation is not con- cerned with sarcasm, irony, humor, or any other usage that might be taken as insincere or not intended literally. Finally, while politeness is one reason why speakers respond in a certain way in certain instances, this dissertation does not focus on politeness in analyzing responses.
1.4 Evidence to test the hypothesis
Evidence to support this hypothesis will come from two sources: natural data from corpora, media and real-life examples; and experimental results to test for common ground inclusion using the template described in (14). Corpora and “wild” sources provide ample evidence of the how response particles can be used in relation to the common ground. The Corpus of Contemporary American English (COCA) and the Linguistic Data Consortium (LDC), particularly the Fisher (Cieri et al., 2004) and Switch- board (Gra f et al., 2001) corpora. All three provide examples from spoken, unscripted English of speakers using response particles and followup content which impact the common ground. The
16 analysis focuses on examining the relationship between the response and the at-issue content, considering interlocutor beliefs and the question(s) under discussion. Experimental data shows some of the information that a corpus study cannot, namely where a given combination of response particle and followup content are not appropriate to speakers. By demonstrating which responses are (in)appropriate under which conditions, the experimental results provides additional support for the Response TargetHypothesis that the at-issue content, beliefs and questions under discussion can be placed in the common ground.
1.5 Preview
Chapter 2 tracks how information moves through discourse, exploring all of the components that are necessary for speakers to meaningfully exchange information. The chapter moves sequen- tially, beginning with the components necessary for information to be exchanged, such as pre- existing common ground and a speaker’s awareness of their audience and context of discourse. The next section looks at the exchange itself, beginning with the assertion and how it shapes the QUD, then continuing to the response, including the response particle(s) and followup content and how these components interact with the at-issue content. The third section looks at the con- sequences of the exchange, particularly whether and how the common ground is updated. The chapter concludes with a discussion of two theoretical models of this process. Chapter 3 looks at the data from corpora, and some natural examples, which demonstrate how responses are used in unscripted discourse. It begins with the non-paradoxical responses which are the rst and second predictions of Response Target Hypothesis presented in (13) and Table 1.2, in which ‘yeah’ plus agreeing followup content places the at-issue content in the com- mon ground and ‘no’ plus disagreeing followup content does not. The rest of the corpus chapter looks at paradoxical responses, ‘yeah’ plus disagreeing followup content and ‘no’ plus agreeing followup content, to address the remaining predictions of the Response Target Hypothesis pre- sented in (13) and Table 1.2. Chapter 4 discusses the experiment and its results in detail. It describes the stimuli, the vari- ables which were manipulated, and the results of the experiment and their consequences for the hypothesis. The nal chapter brings the discussion together by drawing uni ed conclusions from the corpus and experimental chapters. It also discusses the consequences of this research as well as opportunities for further research into paradoxical responses.
17 Chapter 2
Tracking information through discourse
Discourse is a complex and dynamic process which relies on several factors for speakers to suc- cessfully exchange information. All discourse participants must have an idea of what the other participants may or may not know about the discourse situation—who all of the participants are in relation to each other, what they may know about the place and time of the discourse and topics of discussion, and how they can make a meaningful contribution to the discourse. Par- ticipants must constantly reassess each of these variables as the discourse progresses, monitoring new information and whether the participants agree on the truth or accuracy of this information, and whether there is con rmed disagreement. And yet, people do this constantly as they speak to each other every day, unaware of the complexity of the task. This chapter will essentially tell the story of the discourse process, beginning with how speak- ers plan an utterance. It will then look at the discourse itself, separating the utterance and the response as two steps in the process. It will then look at the consequences of the discourse to ex- amine what information was exchanged as a result of the discourse. Tohelp clarify the discussion of how information moves through discourse, I will use the sample discourse in (1), which is a composite of parts of a real conversation.
(1) Sample discourse for tracking information 1. Z: I’m excited about changing schools from Handsworth to Argyle. 2. E: Yeah, that’ll be a good change for you. And it’s so much closer. 3. Z: No, it really is.
18 4. E: But Southerland is a good school, too. 5. Z: No, it’s not, it’s full of drug dealers.
This discourse will serve as a kind of case study to track information through the discourse, af er the theoretical background has been established. It is composed entirely of assertions, no questions, and it contains three response particles with followup content. This sample discourse is rich enough to track information through a number of conditions, but it is straightforward enough to allow for a clear discussion of the management of information.
2.1 Before an utterance
Speakers plan their utterances based on factors such as preexisting common ground and the other discourse participant(s) (Horton and Keysar, 1996; Keysar et al., 2000; Horton, 2005; Horton and Gerrig, 2005; Brown-Schmidt et al., 2008, a.o). Thus before any words are spoken, speakers have put in the e fort to consider what might be an appropriate contribution to the discourse, and this section will look at that planning process.
2.1.1 Sources of Common Ground
Stalnaker (1973) and others had observed that mutual knowledge must have some standing in discourse, because it is clear that speakers can and do rely on information which is not introduced in the previous utterance. It is equally clear, however, that true mutual knowledge, where two speakers have the exact same knowledge, is impossible. Clark and Marshall (1981) were the rst to postulate an account for the “mutual knowledge paradox.” In setting up the problem, Clark and Marshall walk through a number of scenarios to identify the conditions under which Ann could say to Bob,
(2) Ann to Bob: “Have you seen the movie showing at the Roxy tonight?”
They start by supposing that Ann read the movie listings in the morning newspaper. In that situation, the conditions which make (2) felicitous are those in (3), which indicate that the speaker and their addressee have mutual knowledge.
(3) a. Ann knows that t ( lm title) is R (referent, e.g.Monkey Business). b. Ann knows that Bob knows that t is R.
But what if the situation is di ferent? They set up the following scenario:
19 (4) On Wednesday morning Ann and Bob read an early edition of the newspaper, and they discuss the fact that it says that A Day at the Rac is showing that night at the Roxy. When the late edition arrives, Bob reads the movie section, notes that the lm has been corrected to Monkey Business, and circles it with his red pen. Later, Ann picks up the late edition, notes the correction, and rccognizes Bob’s circle around it. She also realizes that he has no way of knowing that she has seen the late edition. Later that day Ann sees Bob and asks, ”Have you seen the movie showing at the Roxy tonight?” (Clark and Marshall, 1981, pp. 12-13)
That scenario needs an additional condition, that of (5).
(5) Bob knows that Ann knows that Bob knows that t is R.
Clark and Marshall create additional scenarios which require additional knowledge states, until arriving at that in (6).
(6) Wednesday morning Ann and Bob read the early edition of the newspaper and discuss the fact that it says that A Day at the Rac is playing that night at the Roxy. Later, Bob sees the late edition and notices the correction of the movie to Monkey Business, and circles it with his red pen. Later, Ann picks up the newspaper, sees the correction and Bob’s red pen mark. Bob happens to see her notice the correction and his red pen mark. In the mirror Ann sees Bob watch all this, but realizes that Bob hasn’t seen that she has noticed him. Later that day, Ann sees Bob and asks, ”Have yo11 ever seen the movie showing at the Roxy tonight? (Clark and Marshall, 1981, p. 14)
This scenario, (6), requires the conditions in (7),
(7) Ann knows that Bob knows that Ann knows that Bob knows that Ann knows t is R. which the authors explain:
Putting herself in Bob’s shoes again, she should reason like this: “Ann knows that the movie is Monkey Business, and she knows that I [Bob] know that too. Yet she believes that I [Bob] think she thinks the movie is A Day at the Rac , and so by her reference, she should think I will decide she is referring to A Day at the Rac .” But if her reference gets Bob to pick out A Day at the Rac , then it is infelicitous (Clark and Marshall, 1981, p. 14)
20 These examples may be convoluted and unrealistic, but the problem that they are demon- strating is that properly assessing another person’sknowledge state in relation to one’sown knowl- edge state is a cumbersome, if not impossible, task. In order to felicitously make any sentence, a speaker must be able to ensure that all of the participants can use the same terms to identify the same referents, but there always exists the possibility of doubt. Demonstrating these di ferent states of knowledge, and the di culty in parsing them, is (8), taken from the TV show Friends.1
(8) Rachel: Ugh, I knew it! Oh I cannot believe those two! Phoebe: God, they thought they can mess with us! They’re trying to mess with us! They don’t know that we know they know we know! (Joey just shakes his head.) Joey, you can’t say anything! Joey: I couldn’t even if I wanted to.
Phoebe and her friends rely on the information mismatch to “mess with” each other, but Clark and Marshall make a more principled objection that relies on processing costs. They ob- serve that for Ann to successfully account for each new level of required knowledge, she would need to be able to process an in nite number of possibilities in a nite (and small—-of en a frac- tion of a second) amount of time, and this is demonstrably impossible. But equally demonstrable is the fact that people say things exactly like Ann’s question all the time. They rely on their own knowledge and their assessment of their interlocutor’s knowledge, based on information which may have changed or may otherwise be misaligned between the speakers; essentially they expect their interlocutor to get their meaning despite any indication that that is a reasonable expectation. That means that an explanation for this paradox must be possible. Clark and Marshall observe that the analyses and terminology are essentially one sided, focus- ing on the speaker and their knowledge; such a focus is perhaps understandable, as the speaker is the source of an utterance, and speakers do not have access to everything that another person knows. Totruly solve the paradox requires a reference to shared, or mutual knowledge, and Clark and Marshall go on to identify three potential sources for this mutual knowledge: community, co-presence and discourse. Community is the source of shared experience or identity; examples of community range from a married couple (a community of two) to all of humanity, and include members of a profession, citizens of a country, or residents of a neighborhood as intermediate examples. Co-presence is derived from the speakers and other objects at the same place and time; this includes the physical environment and the moment in time where the discourse happens. 1This exerpt comes from Season 5, Episode 17, ‘The one where everyone nds out.’
21 Discourse is the information added to the common ground as part of the conversation. These three sources are now broadly accepted as sources for information in the common ground. The crucial insight of Clark and Marshall was that communication relies not on an in nite set of speaker-oriented conditions, but on identi able sources of mutual knowledge. This was signi cant, as it created the possibility of addressee-oriented speech acts in which the message was craf ed with some awareness of the addressee’sknowledge. This insight has informed much of the subsequent research on common ground and related theories. These identi able sources of com- mon ground are important for this dissertation for a number of reasons. These discourse external sources allow for realistic experimental stimuli which mimic something that participants might hear in everyday situations, and for an interpretation of corpus examples that would otherwise be impossible without conjecture and stipulation. The reliance on shared rather than exclusive information not only solves the in nite recursion problem, it also places the common ground squarely at the center of discourse management. Building on Clark’s work, particularly Clark and Marshall (1981), Horton and Gerrig (2005) propose two slightly di ferent labels for describing discourse: commonality assessment and mes- sage formation. In looking at simple exchanges between friends, they observed that information exchanged relies on extensive shared information.
(9) A: I got a letter from Tamar. B: Yes, I told her to write to you.
Even these two lines are notable, they observe, because:
simply having information in common may not be enough—on the surface it would seem that interlocutors must also believe that this information is mutually known. For example, Speakers A and B might both independently have knowledge of Tamar, but for Speaker A to felicitously refer to Tamar in conversation, she would also have to believe that Speaker B knows Tamar as well. Horton and Gerrig (2005, p. 6)
Horton and Gerrig invoke Clark and Marshall’s (1981) idea of co-presence, including within that description the physical, community,2 and linguistic sources of common ground, but they explain that it is not quite su cient because interlocutors still need to think about what infor- mation is “co-present” between them, because it may not be immediately present. For instance, one speaker may observe other people present which the interlocutor may be unaware of; it is 2Horton and Gerrig (2005) depart from Clark and Marshall (1981) by including community membership in co-presence, rather than using co-presence in a strictly spatio-temporal sense.
22 also common that one person does not know what another has in their pockets or bag, but these things are still co-present. The evidence for checking awareness comes from exchanges in which speakers express uncertainty about shared information. This suggests that physical co-presence is not su cient, that speakers must also activate, or recall, the information in the common ground, and interlocutors craf their message with this process in mind. The crucial point is that, while it may seem obvious that speakers operate with the knowledge of their common ground, it is not a priori true. Even if speakers believe that information should be in the common ground, they can- not automatically assume that it is, which is why they must attempt to validate that assumption by checking the common ground. Clark and his many collaborators used data from natural conversations and corpora to under- stand the sources of common ground beyond what is established during discourse. Horton and Gerrig build on this foundation to show that, even with information from these three sources, it still is necessary for interlocutors to check the common ground and to verify that the information is in fact shared.
2.1.2 Context of discourse
Clark and Brennan (1991) raise the issue of the physical constraints on conversations, pointing out that the medium and environment both in uence how responses are interpreted. They identify several constraints which can in uence discourse, and Table 2.1 de nes these constraints and lists examples in which the given constraint plays a role and examples where it does not. The context of a discourse impacts how speakers assess shared information and how they plan their contribution. Clark and Brennan go on to point out that each of these constraints has a potential cost to overcome. Audibility, to take the most relevant constraint for spoken discourse, has a low cost in a quiet o ce with the door closed, for example, but it has a much higher cost in a crowded sports stadium during a game. In other words, it is easy to carry on a conversation in one context and much harder in the other. The higher cost environment also increases the possibility of misun- derstandings and, by extension, the need for the speakers to signal mutual agreement su cient to update the common ground. This matters for responses, because even if an interlocutor said ‘yeah’ in response to an utterance, a speaker still needs to consider whether they can truly consider the information to be in the common ground, given the context in which it was shared. The context of the discourses in Chapters 3 and 4 are con ned to spoken contexts, and as a result there is no Reviewability or Revisability. In some of the corpus data, such as multiperson broadcast conversations, even Sequentiality may not be maintained, as speakers speak over each other and respond to one another out of sequence. As two of the corpora are exclusively from
23 Table 2.1: Constraints on discourse contexts
Constraint Definition Present in Absent in
Copresence A and B share a physical face to face social media environment direct messages Visibility A and B can see each other video chat phone calls Audibility A and B can hear each other phone calls email Cotemporality B receives the message as soon as A face to face, text email, produces it messages voicemail Simultinaeity A and B can send and receive text messages letters messages simultaneously Sequentiality A and B’s turns cannot get out of conversation* text messages* sequence Reviewability B can reread or replay A’s message voicemail, face to face, email video chat Revisability A can revise the contribution email face to face before B receives it
Table adapted from Clark and Brennan (1991, p. 229-230) to include modern forms of communication.
*Conversations may have overlap and other interruptions, but these are usually repaired so that speakers are not “talking over” each other (Sacks et al., 1974). In contrast, text messages can become out of sequence when A and B both introduce, and then respond to, di ferent topics of discussion. telephone calls, these conversations did not have Copresence or Visibility. They may have also had signal disruptions that may have required more e fort on the part of both speakers to accept information into the common ground. Register and genre are also part of the context of a conversation, as they in uence how speak- ers shape their message based on audience and purpose (Yule, 2017). Although there is consider- able disagreement about the divisions between registers,3 Joos’s 1961 “ ve clocks” is still regarded as a baseline division of registers: intimate (inside jokes, declarations of love), casual (conversa- tions between friends), consultative (interviews, teacher/student discussions), formal (lectures, sermons) and frozen (biblical quotes, national constitutions). The rst two registers, intimate 3The most recent register categorization scheme is that of the International Standardization Organization (ISO, 2018), which lists, by my count, 18 registers, including baby talk, vulgar register, bench register, royal register, and others.
24 and casual, assume a large common ground between the interlocutors, while the next two, con- sultative and formal, rely essentially on a common ground based on a shared community based on a subject or topic, although they do not rule out co-presence. The frozen register is for texts and utterances that never vary. The data in the remaining chapters is overwhelmingly of the casual and consultative registers, which shapes the context in which a discourse is happening
2.1.3 Audience design and accomodation
Experimental research into audience design4 shows that speakers mentally “check” that certain information is shared (Galati and Brennan, 2010; Keysar et al., 1998; Pickering and Garrod, 2004, a.o.). In adapting a communicative task to a speci c audience, speakers are necessarily drawing on the common ground that exists between them and their audience. Galati and Brennan (2010) demonstrated that speakers repeating the same narrative will simplify that narrative when they are repeating it for the second time to the same audience. This simpli cation was in evidence at all communicative levels: the number of words used, the level of detail, even the clarity of pronunciation. Speakers know that the audience has already heard the story, which allows them to attenuate the retelling; in other words, they know that the story is in the common ground. There is experimental evidence that speakers resort to “checking” the common ground during discourse, or verifying information contained in the common ground, for example an identity. Keysar et al. (1998) demonstrate that addressees resort to the common ground to verify the iden- tity of potential referents in discourse. Brown-Schmidt et al. (2008) demonstrated that both par- ticipants (their experiments used two speakers) in unscripted discourse use the common ground to craf their message. Speakers will direct questions to a person that they know to have the an- swer, and addressees, i.e. speakers who answer said questions, will consider the available common ground in indicating the correct answer to the question, for example by using a name from the common ground to refer to an individual. Hanna et al. (2003) used eye tracking to determine the extent to which privileged ground, that is information known only to one speaker, interef- ered with common ground interpretation of named objects, and they found that while there is limited interference, when speakers had several potential physical referents available, they almost always looked at the object that was in common, rather than priveleged, ground. This indicates that speakers attend to the common ground in discourse, relying on the information that they know to be shared between both interlocutors. 4I am using “audience design” here as an envelope term to capture the many ways in which interlocutors adjust their language usage and understanding around those to whom they are speaking.
25 Pickering and Garrod’s (2004) work on backchanneling also demonstrates the reliance on common ground, in that the signals that a speaker of a monologue receives from their interlocu- tor can help them to direct attention to previous information or to future utterances; in essence speakers are constantly adjusting their message based on (of en non-verbal) signals of their in- terlocutors, such as nods or even simply continued attention. Pickering and Garrod term this process alignment, in that the interlocutors are aligning the information that is shared between them, checking that previous information is in fact established as shared—in other words, that it is in the common ground. The mechanism of how this works will be explored more fully in the following section, but the use of alignment checks that previously mentioned information is indeed established in the common ground through the use of backchanneling and other signs of acceptance. These studies, and others like them, provide evidence that speakers are aware of shared infor- mation as they construct their utterances. They also demonstrate the extent to which this shared information—the common ground—is used to communicate with those with whom this in- formation is shared. This research demonstrates, through the use of methodologies such as eye tracking, that speakers can and do use cues beyond propositions, including previous shared ex- perience and continued attention, to shape how they speak to others. While the data in Chapters 3 and 4 will not allow for these cues to be used in my analysis, the research into audience design suggests that considering information beyond the utterance, perhaps evidence of the emotional state or the dispositions of the speaker(s), may allow for a richer understanding of how response particles are involved in managing the common ground.
2.1.4 Modeling the state of information before the utterance
Recalling the sample conversation in (1), the state of the discourse before anything is said is as indicated in (10) line 0; no one has spoken. This is the planning stage, in which the rst speaker is assessing the common ground, time and place, and their interlocutor in order to plan an appro- priate contribution.
(10) Sample discourse for tracking information—Before the utterance 0. No speaker; Z is planning her contribution to the discourse 1. Z: I’m excited about changing schools from Handsworth to Argyle. 2. E: Yeah, that’ll be a good change for you. And it’s so much closer. 3. Z: No, it really is.
26 4. E: But Southerland is a good school, too. 5. Z: No, it’s not, it’s full of drug dealers.
In order for Z to plan a successful utterance, she must have some expectation, and a basis for that expectation, about what E knows. This is the pre-existing common ground, and since the discourse is about schools in a given community, then the source for this pre-existing common ground is shared community. The setting of the discourse also shapes how Z will prepare her utterance; Z may hesitate to make these statements at Handsworth or Southerland, where some- one overhearing the conversation would likely be o fended. The register is also fairly informal, as if Z and E are friends, or at least friendly acquaintances. Z also considers her larger audience, not only E but anyone else who may reasonably be considered as part of this conversation (there was a third participant in the conversation from which this is extracted, namely N.), and Z must also consider these other discourse participants’ available information. In order to ensure that her utterance is felicitous, Z must consider all of these factors. She cannot felicitously make the same utterance to people who do not share community membership, for example. This utterance would also be infelicitous in another context, for example if Z were a customer in a restaurant and E were her server; even shared community membership and a friendly relationship between the speakers would not make that utterance appropriate in that situation.
2.2 The exchange, a two-step process
Conversation is necessarily cooperative, in that the expansion of the common ground is the result of speakers taking turns to make contributions, accept or reject new information and build on the information previously established in the common ground; crucially, this process requires at least two speakers. The evidence of cooperation appears throughout the discourse. Clark and Wilkes-Gibbs (1986) look more speci cally at how discourse participants arrive at a mutually shared interpretation of nominal referents, but they draw on the work of conversation analysts (Scheglo f, 1982; Sachs, 1967; Sacks and Scheglo f, 1973; Sacks et al., 1974; Gazdar, 1979; Kamp, 1981, a.o.) that shows how conversation is constructed. If, for example, two speakers are talking in a loud environment, and the listener does not hear the information that established a discourse referent, the listener can stop the conversation and con rm the identity, or they can wait until the speaker’s turn is nished. Alternatively, they can infer the identity of the referent and sig- nal their understanding through back-channeling, explicit repetition, or other means. Crucially, Clark and Wilkes-Gibbs observe, the process is collaborative. They continue:
27 To some it may appear self-evident that the process is collaborative, but it is one thing to assume it is and quite another to understand why it is and how it works. The goal here is important, since, if conversation is fundamental, its processes are likely to underlie or shape those in other uses of language as well. Clark and Wilkes-Gibbs (1986, p. 3)
Clark and Wilkes-Gibbs also observe a bit of a paradox, in that speakers use the least amount of e fort to achieve their communicative goals. So how do they manage collaboration when both are conserving their e forts? Clark and Wilkes-Gibbs propose that the collaboration happens in a two phase process: pre- sentation and acceptance. One speaker must present a referent, and the other speaker must accept it in such a way that it is clear to both that the referent is mutually accepted. This may be done passively, by allowing the conversation to continue with the referent unchallenged, or it can be done more actively, with a signal such as a response particle. Both represent collaboration strate- gies on the part of the interlocutors. Clark and Wilkes-Gibbs also demonstrated this mutual establishment of referents experi- mentally by having pairs of speakers sit on opposite sides of a partition, each with a set of tangrams that they would then direct each other in arranging so that the two sets matched, according to a set of provided images. The speakers could not see their partner’s set of tangrams, but they were able to speak back and forth across the screen wtih no di culty. When the director (the speaker instructing the other speaker) rst described a gure, they used an average of over 40 words per image (the referent). During the second description, they used half as many words, and by the sixth description they used an average of less than 10 words per image. While Clark and Wilkes-Gibbs do not provide transcripts of their data, the speakers might use descriptions similar to those in (11).
(11) 1. the one that’s kind of a blue color, kind of medium size, with a big slope on the sides so that if it were sitting on the table upright it would lean, but there’s a big slope and a little slope (41 words) 2. the blue kind of medium size one with the di ferent size sloping sides, the one that would lean if it stood up (22 words) 3. the medium size blue one with the sloping sides (9 words)
The researchers’ conclusion was that such a reduction in e fort was possible because the speak- ers knew the referent to be in the common ground by virtue of having mutually agreed to a map-
28 ping between a discourse referent and the object it refers to over the course of the conversation (Wilkes-Gibbs and Clark, 1992, p. 13). Crucially, the experiment also demonstrated that the in- formation contained in the common ground may be utilized at a later part of the discourse. In later chapters of this dissertation I will rely on the fact that speakers take the common ground into account when formulating their utterances and responses, when I investigate the use of re- sponse particles in discourse. We will see that, by taking the common ground into consideration, speakers are able to respond to targets other than the at-issue content; for example, speakers may accept or reject a question under discussion based on whether an answer is already available in the common ground. That discourse is collaborative is crucial to many of the models that follow (see in particular Farkas and Bruce (2010); Malamud and Stephenson (2014)), and to the claims in this disserta- tion. The responses that may impact the common ground are all part of the acceptance phase of the discourse; without some signal of information being accepted into the common ground, it cannot be concluded that the common ground has been expanded.
2.2.1 The assertion
The contribution that a speaker prepares can take the form of a question, exclamation or asser- tion, and it may represent an illocutionary act (for example, intended to inform or persuade) or a perlocutionary act (intended to indirectly invoke an action on the part of the listener). The current inquiry will con ne itself to illocutionary assertions, that is statements (not questions) that are intended to share information and not suggest action. The speci c assertive statements that a speaker o fers constrains the course of the subsequent discourse by contributing di ferent kinds of information, such as presuppositions, at-issue con- tent, and question(s) under discussion. The information in the assertion is the result of the plan- ning phase which considers the pre-existing common ground and the context of the discussion, and it limits how the interlocutor can respond while honoring the maxim of relevance. This sec- tion will consider how speakers use these types of information to re ect and shape the common ground.
2.2.1.1 The at-issue content
A declarative utterance may introduce at least two types of propositional material, presupposi- tions and at-issue content. Presuppositions are the information which is assumed to be true in order to evaluate the truth conditions of the proposition, while the at-issue content is considered
29 the “main point of the utterance” (Abbott, 2000). One of the most signi cant di ferences is how presuppositions and at-issue content interact with the common ground. Two of the key diagnostics to assess at-issueness are the ability to respond directly to the at- issue content and the ability to directly challenge the at-issue content, properties not shared by presuppositions (Tonhauser, 2012).The example in (12) demonstrates that a direct response will target the at-issue content.
(12) A: Juan lives in Maria’s house. B: Yeah. Interpretations: Yeah (it’s true that Juan lives in a house owned by Maria) Yeah (it’s true that Maria owns a house) adapted from Tonhauser (2012)
The at-issue content in A’s utterance is that Juan lives in a speci c house, the preuspposition is that the house is owned by Maria. If an interlocutor simply responds with ‘yeah’ or ‘yeah, it’s true,’ that response will be interpreted as accepting the at-issue content concerning Juan’s residence. This holds true with ‘no’ responses as well.
(13) A: Juan lives in Maria’s house. B: No. Interpretations: No (it’s not true that Juan lives in a house owned by Maria) No (it’s not true that Maria owns a house) adapted from Tonhauser (2012)
Rejection in response to the assertion will again target the information concerning Jaun’sresi- dence, not Maria’sownership of a house. In fact it may be well established in the common ground between A and B that Maria does indeed own the house in question. The at-issue content, the main point of Speaker A’s utterance, is whether Juan lives in the house in question, and both positive and negative responses target this information. Negative responses represent a challenge to the truth of the at-issue content, and they can be followed by contrary claims, as (14) demonstrates.
(14) A: Juan lives in Maria’s house. B: No, he lives in his father’s house.
30 This is not to say that a presupposition cannot be challenged, but doing so usually requires something beyond direct agreement or denial, for example a “wait a minute!” response (von Fintel, 1994; Tonhauser, 2012, a.o.). In order to challenge a presupposition, a speaker needs to signal a shif away from the at-issue content, as in (15).
(15) A: Juan lives in Maria’s house. B: Wait a minute! Maria doesn’t own a house, she sold it last year.
This dissertation is agnostic on whether all presuppositions are necessarily in the common ground, but it treats presuppositions as shared information unless they are explicitly challenged. For example, if a man and a woman are guests in a hotel and the desk clerk says to the woman, “A package came for you; I gave it to your husband.” the clerk is presupposing that the man is the woman’s husband. However, even if the man is not the woman’s husband, the at-issue content is still clear, and the referent of “your husband” is clearly the man traveling with the woman, even if that label is not applicable to him. Whether the false presupposition is challenged or not is up to the woman, but it does not impact the at-issue content concerning the package. Unless challenged directly, presuppositions similar to this one can at least be considered as part of the shared information for the immediate discourse, even if they are not mutually agreed to as true. In other instances, presuppositions are based on information already in the common ground, of en as a result of shared community membership. As an example, if a person says to their co- worker, “The boss will be late today, she totaled her pick-up.” the presuppositions concerning the boss’sgender and ownership of a pick-up truck are based on their shared community membership as employees of the same organization. These presuppositions still cannot be directly responded to or challenged.
2.2.1.2 The question under discussion (QUD)
The question under discussion (QUD) results from the work of Roberts (1996, 2011) to recon- cile several previous ideas in the literature, chie y that of relevance from Grice (1975), Ginzburg’s (1996) notion of QUD as a partially ordered set, and Stalnaker’s(1978) idea that the goal of conver- sation is to exchange information about the world, i.e. to discover “the way things are” (Roberts, 2011, p. 64). The claim is that discourse intended to exchange information does so in answer to an unspoken question, the QUD, and this question constrains the discourse by requiring relevant contributions in response to said question. Roberts recognizes that within the big question goal are smaller, more immediate goals, which she terms domain goals, that constrain the question under discussion to be something more immediately relevant to the agenda of the conversation
31 at hand. She goes on to develop the QUD as an important tool in understanding information structure at the semantic and pragmatic level. To clarify the QUD, let us consider an example, (18), in which the utterance in line 1 estab- lishes the Domain Goal QUD in line 2. Under Roberts’ theory, the Doman Goal QUD is the most immediate QUD which stems from an uttterance; it is the QUD to which the speakers have committed to nding an answer, as it is a mutually agreed to QUD.
(16) An illustration of QUDs in relation to utterances and responses 1. Utterance: Carole was invited to the party. 2. QUDDG: Was Carole invited to the party? Domain Goal
Because the QUD is actually a partially ordered set (Ginzburg, 1996), it becomes necessary to discuss the questions in that set separate from one another. To this end, Roberts and her collaborators use the term Current Question (Simons et al., 2016; Beaver et al., 2017), which they de ne as:
(17) Current Question (CQ) for an utterance: The CQ for an utterance is a privileged subset of the focal alternative set of the uttered sentence which meets the following conditions: 1. The proposition expressed is a member of the CQ and 2. The CQ has at least one additional member (Simons et al., 2016, p. 8)
In answering the Domain Goal QUD, the utterance in line 1 also provides partial answers to the higher level goals in lines 3 and 4. The potential responses in lines 5-7 are felicitous because they respond to the higher, or Current, QUD, whereas the answers in lines 8 and 9 do not respond to the QUD and therefore violate the maxim of Relevance.
32 (18) An illustration of QUDs in relation to utterances and responses 1. Utterance: Carole was invited to the party. 2. QUDDG: Was Carole invited to the party? Domain Goal 3. QUDCQ Who was invited to the party? Higher (Current) Level 4. QUDSoW What is the state of the world? State of the World 5. Response: But Linda wasn’t. 6. Sharon was too, but she didn’t come. Answer QUDCQ 7. That’s surprising that Carole was invited instead of Jen. 8. The champagne was excellent. Don’t answer QUDCQ 9. I hate parties dance music.
Simons et al. (2016) also point out that discourse is, by its nature, restricted contextually, pointing out that if Mark’s daughter went on a bird watching trip during the day, and then he asked her, “What did you see?” he is asking about the birds that she saw that day, not the other random objects that she likely sees every day. With this analysis in mind, it becomes apparent how the question expressed in (18) line 3 can be classi ed as a Current Question. This set is represented in (19)
(19) CQx = {p1, p2, p3, p4, …}, where x = “Carole was invited to the party.” and p1 = “Sharon was invited to the party.” and p2 = “Diego was invited to the party.” and p3 = “Stevie was invited to the party.” and other potential answers.
One challenge for using QUD in analysis is that there can be more than one QUD that satis- es the criteria of a Current Question; “Carole was invited to the party” also provides an answer to the CQ “What has Carole been invited to?” Roberts (2011) provides an example from a pos- sible world which contains only two people, Robin and Hillary, and only two foods, bagels and
33 tofu. She illustrates the possible QUD Stack, that is the ordered set of potentially answerable Questions Under Discussion. In the tightly constrained world of this example, the number of questions is necessarily constrained, the maximal being “Who ate what?” and the minimal being along the lines of “Did Hillary eat tofu?” Roberts (2011) demonstrates that the QUD must be accepted by both interlocutors, and this opens up the possibility of targeting the QUD without answering it. One of my core proposals in this dissertation builds on this idea of accepting a QUD and takes it further to become a hy- pothesis about the role of RPs. My proposal is that RPs can target the QUD, in the sense that they can not only signal an answer to the QUD (agreement/disagreement with the at-issue con- tent of the previous utterance), but they can also directly target the legitimacy of the QUD itself. They can either accept it as a good QUD to which an answer is not available (yeah), or they can reject it as a QUD because it is either not relevant or has already been answered (no).
2.2.1.3 Statements of belief
An important question for this dissertation is whether RPs can respond to an opinion as well as a fact. Convertino et al. (2008) calculated that, out of 13 potential dialog acts, the three most common in their data were signalling a speaker’s judgement, a speaker’s agreement with a prior judgement, or were acknowledging receipt of information; these were signi cantly more fre- quent than conversational moves which may be regarded as typical, such as introducing new information, querying another speaker or replying to a query. Speakers can felicitously disagree with statements of opinion (Umbach, 2014), signalling that they hold a di ferent opinion, not that another’s opinion is wrong. Responses can resolve statements of fact (Ginzburg, 1995a,b) for inclusion into the common ground, and the question here is whether and how response can target statements of belief for inclusion into (or exclusion from) the common ground. An answer to this question requires in inquiry into the nature of beliefs in language. Laser- sohn (2005) demonstrates that beliefs need to be indexed to an individual. Lasersohn describes how the truth of an utterance such as “The chili is tasty.” necessarily relies on whether or not the speaker does in fact nd the chili tasty; this proposition may be true for John but not for Mary (Lasersohn, 2005, p. 649). Crucially, John’s utterance of “The chili is tasty.” and Mary’s utterance of “The chili is not tasty.” can both be true, because the utterances index to di ferent individuals who may evaluate the chili di ferently, resulting in faultless disagreement (Umbach, 2014). The importance of this insight for the Response Target Hypothesis is that it provides a the- oretical basis for positing beliefs for inclusion into the common ground. If John claims that the chili is tasty and Mary disagrees, it is intuitively correct that both beliefs should enter into the
34 common ground. This becomes apparent when one of the speakers accesses the common ground concerning the chili at a later point in the discourse, as in the hypothetical conversation in (20).
(20) John: The chili is tasty. Mary: No, the chili is not tasty! John and Mary have discussed the chili, and then their friend Peter arriv and asks them about the chili. Peter: What do you guys think of the chili? John: It’s tasty, although Mary doesn’t think so.
If we applied a conventional model of common ground update, and did not adopt a Lasersohn- style approach in which taste predicates are relativized to the beliefs of individuals, John’sresponse to Peter should be impossible. Mary disagrees with John, and this should result in a crisis and leave the common ground un-updated. John’s response is felicitous, however.5 Contrast this with the response in (21), which would re ect an un-updated common ground, i.e. in which John’s and Mary’s individual beliefs about the tastieness of the chili were not entered into the common ground. The example includes two potential responses that John could give to Peter. Although Mary has indicated that she does not nd the chili to be tasty, John has indicated that he does, and therefore it would be awkward for him to answer that he does not know what he thinks of the chili. It still might be awkward for him to respond that the chili is tasty when he knows that this is not a universally held view, but it does at least re ect what he thinks of the chili. Peter’s question in (21) concerns the opinion(s) of the group, and we can understand that there is no single opinion because we know that John and Mary do not agree. So niether of the responses is entirely felicitous.
(21) John: The chili is tasty. Mary: No, he chilly is not tasty! John and Mary have discussed the chili, and then their friend Peter arriv and asks them about the chili. Peter: What do you guys think of the chili? John: ??I don’t know. / ?It’s tasty.
But let us consider the predictions of a theory that did not allow information about individual beliefs to be entered into the common ground. The disagreement would mean that the common 5The experimental stimuli in 4 are similar to (20), and the data show that other speakers also nd such an ex- change felicitous.
35 ground would not be updated with any information about the chili’s tastiness according to either John or Mary. Therefore if Peter asks a question that requires information from the common ground to answer, then an answer of “I don’t know” should be appropriate. This will be true of any question that requires information from the common ground. The obvious problem is that English speakers will expect that John can answer Peter’squestion with something other than “I don’t know.” This suggests that, not only is the common ground updated during John and Mary’s exchange, it is updated to include both of their evaluations of the chili. That would mean that the common ground is updated to include two beliefs, both indexed to an individual speaker and the time of utterance. If an utterance including a taste predicate is indexed to a group, then it should be true of all of the members of a group. Therefore, if John and Mary and others are on a roller coaster, and John says, “This is fun!”, that cannot apply to the group if Mary then says, “This is not fun!” It is for this same reason that John cannot respond as in (22).
(22) John: The chili is tasty. Mary: No, the chili is not tasty! John and Mary have discussed the chili, and then their friend Peter arriv and asks them about the chili. Peter: What do you guys think of the chili? John: (?)We think it’s tasty. / (?)We think it’s not tasty.
Both of John’spotential responses are infelicitous here, because he knows that the group iden- ti ed by “we,” namely Mary and himself, do not collectively nd the chili to be tasty (or not tasty). John can felicitously respond to Peter’s question in one of three ways, either by reporting his own evaluation of the chili, reporting Mary’s evaluation, or reporting both of their evaluations. Lasersohn (2005) proposes a detailed analysis of the semantics of belief, in which expressions which encode belief, such as scalar predicates, are indexed to a person, usually the speaker. The Response Target Hypothesis proposes a more generalized version, that in (23), whereby not just scalar predicates, but any assertion at all, places information about the speakers’ beliefs into the common ground.
(23) Belief{speaker, time}
So just as when John says ‘the chili is tasty’, the common ground gets updated with ‘John thinks the chili is tasty to him,’ similarly when John says ‘it’s raining,’ the CG gets updated with
36 ‘John thinks it’s raining’. Belief can be placed in the common ground, including through the use of response particles, as in the exchange in (24), a real exchange between two anonymous members of a popular online forum about cars.
(24) Context: Two individuals are discussing a news story in which a stunt motorcyclist named Kats w injured while attempting to jump h motorcycle between two buildings while a moving train occupied the gap between the buildings. α: Truth is, the only person who’s [sic] life is at risk [with the stunt] is Kats himself . β: Yeah, no. This shit is obviously potentially dangerous to others. Want to do insane jumps that’s ne, but do it in a location where there aren’t other people around.
β’s response of ‘yeah’ seems to be targeting α’s belief that no other people are at risk from Kats’s stunt, because β then says ‘no’ and follows up with content that unabiguously disagrees with α’s assertion. This example is also illustrative in that it does not include a taste predicate, but it still shows that a speaker (in this case, α) may hold a belief that others may identify as objectively wrong while still accepting α’s belief. Lasersohn (2005) also discusses situations in which the truth is externally veri able and re- quires no indexing to an individual or group.
For example, if I taste the chili, I might conclude from the avor that it contains pork, and utter (25):
(25) The chili contains pork. In saying this, I am in some sense expressing my personal opinion; but it is a very di ferent case from [previous] examples…, because there is a fact of the matter as to whether the chili contains pork. Lasersohn (2005, p. 644)
Regardless of whether the chili contains pork or not, the speaker of (25) may believe, against any evidence, that it does contain pork. Under my proposal, this belief, if uttered in discourse, may become part of the common ground, and the data in Chapters 3 and 4 demonstrate how response particles help to achieve this. That speaker beliefs are entered into the common ground as shared information nds sup- port in Kri a’s (2015) concept of commitment space. Even if the at-issue content of a given ut- terance is not entered into the common ground, the speaker’s discourse commitment is.
37 2.2.1.4 Modelling the utterance
Torevisit the sample discourse (1), the state of the discourse has moved from planning to speaking; the utterance in line 1 has been uttered.
(26) Sample discourse for tracking information—Af er the utterance 0. No speaker; Z is planning her contribution to the discourse 1. Z: I’m excited about changing schools from Handsworth to Argyle. 2. E: Yeah, that’ll be a good change for you. And it’s so much closer. 3. Z: No, it really is. 4. E: But Southerland is a good school, too. 5. Z: No, it’s not, it’s full of drug dealers.
The assertion in line 1 contains several components, summarized in (26’).
(26’) The components of (26) line 1 Utterance: Z: I’m excited about changing schools from Handsworth to Argyle. Presuppositions: 1. There are two schools, Handsworth and Argyle. 2. Z currently attends Handsworth. 3. Z will attend Argyle. at-issue content Z is excited about changing schools from Handsworth to Argyle. QUDDG Is Z excited about changing schools from Handsworth to Argyle? QUDCQ1 What is Z’s attitude about changing her schools? QUDCQ2 What is the attitude of students when they change their school? Z’s belief Z believes that changing schools from Handsworth to Argyle is ex- citing.
The Domain Goal QUD is the most immediate QUD to which the at-issue content provides an answer. However, this does not mean that the at-issue content can only answer the Domain Goal QUD; it can answer the slightly broader QUDCQ1, but so can other assertions. Answers to QUDCQ1 are limited to assertions which re ect Z’s attitude to changing schools and which do not contradict the answer to the Domain Goal QUD, such as those in (27).
38 (27) Potential utterances which answer QUDCQ1 1. Z: But I’m a little nervous because it’s a much harder school. 2. Z: I am dreading catching the bus at 6:15 though. 3. Z: Finally going to the same school as my boyfriend will be great. 4. and other possible utterances
Extending the QUD to QUDCQ2, the potential assertions which answer this question must re ect the attitude of students who may or may not be Z towards changing their schools, such that they do not contradict the answers of QUDCQ1; this is part of the denotation of the question. This does not mean that no other student can express apprehension or fear or something else other than excitement; it means that, within this conversation, an answer to QUDCQ2 cannot contradict the at-issue content that Z is excited.
(28) Potential utterances which answer QUDCQ2 1. Z: I’m really excited. 2. M: I’m exhausted with changing schools so of en. 3. N: Changing schools is exhausting but also kinda fun. You get to meet new people, it’s good. 4. O: I don’t want to change schools, I love my school! 5. P: I’m really happy to get to go to a school that focuses on the arts. 6. and other potential utterances
As the QUD becomes broader, the set of utterances which can provide an answer to the QUD becomes larger. The broadest or highest level QUD is “What is the state of the world?”, to which the answers are in nite. What this all demonstrates is that even a simple utterance introduces several components that another speaker can respond to. Z introduces several propositions, including at-issue content, presuppositions, her belief, and several QUDs, all of which are available for an interlocutor to respond to. The next section will focus on how interlocutors assess the various propositions and respond to one or more p with the use of response particles.
39 2.2.2 The response
Clark and Schaefer’s (1989) work on common ground has in uenced all subsequent work on the topic, mine included. Their seminal article on the development of common ground was the rst to describe the grounding criterion: “The contributor and the partners mutually believe that the partners have understood what the contributor meant to a criterion su cient for current purposes.” (Clark and Schaefer, 1989, p 262). This means that, in order for the information to be considered as in the common ground, it is necessary for all of the conversational partici- pants to believe that they all share at least some level of shared understanding. In addition to the non-linguistic information in the common ground developed by Clark and Marshall (1982), this de nition stipulates that the common ground includes the linguistically negotiated information that has met the grounding criterion. The criterion is intentionally lef fairly malleable so that it may be adaptable to a wide range of discourse contexts, but crucially it relies on some kind of evidence of su cient understanding, each dependent on how that understanding is signalled. To that end, Clark and Schaefer propose ve levels of evidence:
1. Continued attention. B shows he is continuing to attend and therefore remains satis ed with A’s presentation. 2. Initiation of the relevant next contribution. B starts in on the next contribution that would be relevant at a level as high as the current one. 3. Acknowledgement. B nods or says “uh huh,” “yeah,” or the like. 4. Demonstration. B demonstrates all or part of what he understood A to mean. 5. Display. B displays verbatim all or part of A’s presentation. (Clark and Schaefer 1989, p 268)
These ve types of evidence can be captured in one simple generalization, that expantion of the common ground is a two phase process (Clark and Brennan, 1991):
(29) a. Presentation phase: A presents utterance u for B to consider. He does so on the as- sumption that, if B gives evidence of e or stronger, he can believe that she understands what he means by u. b. Acceptance phase: B accepts utterance u by giving evidence e that she believes she understands what A means by u. She does so on the assumption that, once A registers that evidence, he will also believe that she understands.
Following on the work of Clark and his collaborators, it is not su cient to view the common ground as a monotonic data set in which each element is included as the result of x or x + y,
40 where x is an utterance and y is a response. Rather its construction relies on the evidence that the discourse participants provide that the information is mutually shared. Moreover, the strength of evidence has consequences for the extent to which a speaker may conclude that information is in the common ground. If my interlocutor repeats what I say verbatim, I can have a high degree of certainty that the common ground has indeed been updated, but if my interlocutor simply sits quietly and continues to listen, I may expect that the common ground has been updated but cannot rule out the possibility that their mind was wandering and they missed what I said. Therefore, while I may assume that the common ground has been updated, and proceed based on that assumption, conversations such as that in (30),6 in which Emma is “continuing to attend” to the speaker’s monolog but clearly misses parts of it, cannot be ruled out.
(30) Context: Holden talking about h personal background with Emma, who listening quietly and appearing attentive by looking at Holden while he speaks. Holden: […]I’ll just tell you about this madman stu f that happened to me around last Christmas just before I got pretty run-down and had to come out here and take it easy. I mean that’s all I told D.B. about, and he’s my brother and all. He’sin Hollywood. That isn’t too far from this crumby place, and he comes over and visits me practically every week end. He’s going to drive me home when I go home next month maybe. He just got a Jaguar. One of those little English jobs that can do around two hundred miles an hour. It cost him damn near four thousand bucks. He’s got a lot of dough, now. He didn’t use to. He used to be just a regular writer, when he was home. He wrote this terri c book of short stories, The Secret Gold sh, in case you never heard of him. The best one in it was “The Secret Gold sh.” It was about this little kid that wouldn’t let anybody look at his gold sh because he’d bought it with his own money. It killed me. Now he’s out in Hollywood, D.B., being a prostitute. If there’s one thing I hate, it’s the movies. Don’t even mention them to me. Emma: Wait, who’s D.B.? Is D.B. why you hate the movies?
To visualize the di ference between a common ground based on levels of evidence versus one that is not, consider Figs. 2.1 and 2.2, in both of which xn represents information, green is the common ground, and pink is the priviledged ground of Speakers A and B. In both gures, both A and B can safely regard x4 as being in the common ground. In the gradient common ground, 6The narrative text is the second half of the opening paragraph of J.D. Salinger’s The Catcher in the Rye, origi- nally published in 1951.
41 A x1 x2 x3 x4 x5 x6 x7 B
Figure 2.1: Gradient conception of common ground
A x1 x2 x3 x4 x5 x6 x7 B
Figure 2.2: Non-gradient conception of common ground
Fig. 2.1, it is likely x3 and x5 are in the common ground as well, but x2 and x6 may not be, and x1 and x7 are almost certainly not in the common ground. This contrasts with the conception of the common ground depicted in Fig. 2.2, in which x2 and x6 are de nitely excluded from the common ground and x3 and x5 are de nitely included. For information where there is some degree of doubt about its inclusion in the common ground, speakers can then check whether xn is in the common ground or not; one strategy is to simply ask, “We agreed about xn, right?” This checking function also conditions responses, as Speaker A may believe that information is not in the common ground even though it is; in such cases Speaker B’s response will re ect that they’ve checked the common ground. An example of such a dialog is that in (31).
(31) 1. Nina: My date last Saturday went really well. 2. Jane: Yeah, you were saying. 3. Nina: Oh that’s right! I told you yesterday…
In line 2 of (31), Jane tells Nina that the information in line 1 is already in the common ground, but in order to tell Nina this, Jane must herself must be certain of what is already in the common ground; in short she must check the common ground. In (31) it is as simple as remembering that Nina already told her about the date. But the simplicity does not invalidate the necessity. The discussion to this point has demonstrated how the response operates to provide evidence for accepting information into the common ground. The next section will explore an interlocu- tor’s options for responding to an utterance.
42 2.2.2.1 Response options
Bare response particles, ‘yeah’ and ‘no’, can accept or reject information from a previous utter- ance. This may sound trivial, but the mechanisms behind response particles are more compli- cated, not least because a successful theory must account for paradoxical responses. Two ideas, the ellipsis and anaphor theories, have explained many of the features of response particles. This section will consider both before demonstrating the utility of the anaphor based theory. Example (32) builds on the previous section’s notions of a two phase expansion of the com- mon ground, but this is an example of the common ground failing to be updated.
(32) A: Did you know mother had been drinking? B: I don’t think mother had been drinking at all. (Clark and Brennan, 1991, p. 225)
B signals that she has understood A, because she o fers a relevant next turn. But the content of her turn makes clear that she has not accepted the content of A’s utterance. A’s utterance is a question about B’s knowledge, but A introduces the presupposition that mother had been drinking. Instead of answering the question directly, B challenges the presupposition contained within the utterance. B’sresponse makes it clear that she did indeed hear what A said, but whether the common ground has been updated, and if so with what, is not certain. The presentation and acceptance phases are central to testing the claims of this dissertation, in that the template in (33) is the structure of the exchanges used in the experimental stimuli and in collecting the corpus data (see Chapters 3 and 4 respectively).
(33) Adjacency pair template α:. Utterance contains a proposition consisting of at-issue content and representing α’s belief and discourse commitment, and responding to an understood QUD β:. response particle ‘yeah’ or ‘no’, and followup content that agrees or disagrees with the at-issue content contained in α’s utterance
The presentation phase, the Utterance, introduces the information, and the acceptance phase, including the response particle and followup content, responds to that utterance. The compo- nents of the response target the content of the utterance. The insight from Clark and Brennan (1991) is that, while the response signals comprehension by default, this is not the same as accep- tance. The distinction between the response particle and the followup content is recognized by Pas- quereau (2018), in his discussion of response particles and what he terms as coda. In discussing
43 the use of response particles and their codas in European French, he points out that the coda does not equal the answer; this is consistent with my analysis in which the two components, the response particle and content, can respond to di ferent targets.
2.2.2.2 The use of response particles
The previous section explored the nature of responses in general, and this section will turn to resopnse particles in particular. Response particles are frequently used in isolation in response to an utterance or question, such as in (34).
(34) 1. Ben: I think the movie starts at 8. 2. Matt: Yeah. 3. Ben: So we’ll leave in an hour. 4. Matt: No. 5. Ben: Why not? 6. Matt: I’ve seen it already.
The response particles in lines 2 and 4 are su cient to accept information into the com- mon ground (the movie starts at 8) and reject information (and a suggestion) from the common ground (they will not leave in an hour). How this is possible with bare response particles will be discussed in this section. A feature of English response particles is that both ‘yeah’ and ‘no’ can respond to utterances with negation (Pope, 1972; Umbach, 2014, a.o.). This relates to the properties of relative and absolute polarity, a feature which will also be explored in this section.
RPs as anaphors and RP elipsis
In examining prps in isolation, Kramer and Rawlins (2008) o fer an account of how ‘yes’ and ‘no’ respond to questions. Responding to positive questions, ‘Is Alfonso coming to the party?’ is straightforward enough, in that, as shown in Fig. 2.3a, the response particle ‘yes’ is what remains af er the rest of the sentence is elided. Accounting for ‘yes’ as a response to a negative question requires a bit more structure, as shown in Fig. 2.3b. The [e] in ΣP is a feature that allows for TP to be elided while ‘yes’ remains. Holmberg (2012) expands this proposal with the idea that Σ, which he terms Pol(arity), can have one of three values: a rmative, negative, and open (that is, neither a rmative nor negative).
44 (a) ‘Is Alfonso coming to the party?’ (b) ‘Is Alfonso not coming to the party?’ Kramer and Rawlins (2008, p. 1) Kramer and Rawlins (2008, p. 4)
Figure 2.3: ‘Yes’ as isolated response to positive and negative questions under Kramer and Rawlins (2008)
Under his analysis, the response in (35) has undergone movement, as in Fig. 2.4, which depicts a positive response.
(35) Q: Is Alfonso coming to the party? A: Yes. (Holmberg, 2012, p. 58)
Figure 2.4: Tree of ‘yes’ response in (35)
Holmberg also makes the point that di ferent languages employ di ferent strategies for of- fering polar responses. For positive responses, a language may employ a dedicated particle(s) like English and French, or it may repeat the content verb to signal agreement with p, like Russian and Mandarin. Negative responses are also implemented di ferently in di ferent languages, with some languages like Romanian employing multiple negative particles that are in complementary distribution (Roelofsen and Farkas, 2015). Pasquereau (2018) builds on Holmberg’s analysis by proposing that response particles instantiate a Pol head in the syntax which can then move into focus position to scope over the entire clause. The ellipsis approach captures the facts of bare response particles, but it struggles to account for paradoxical responses. Because the two parts of the response contradict each other, it seems problematic that they could both have the same phrase elided, which is what Figs. 2.3 and 2.4 would predict. In essence, paradoxical responses are a problem for the ellipsis approach, because it tries to assert the truth of two mutually exclusive ps, a problem which is clear in (36).
45 (36) Paradoxical response to an utterance, with elided content spelled out Q. Alfonso is coming to the party. yes. #Yeah [he is coming to the party] he isn’t [coming to the party]. no. #No [NEG he is coming to the party] he is [coming to the party].
The alternative hypothesis is an anaphor based account of bare response particles, but it is not immediately clear that it provides a solution to the paradox problem. Using (37) as an illustration, an anaphor based account still requires that Sylvia’s response contain con icting information, that the dinner was both terrible and alright.
(37) Situation: Gustav and Sylvia went to a friend’s home for a meal. Gustav did not care for the food, but Sylvia found it appropriate. 1. Gustav: That dinner was terrible. 2. Sylvia: Yeahthat dinner was terrible, I thought it was alright.
Kri a (2013) o fers a solution to this problem by developing an analysis which allows re- sponses to be anaphors for more than one potential target. Kri a demonstrates that clauses in- troduce a number of discourse referents that anaphors can be anchored to, demonstrating each with the use of ‘it’ or ‘that’:
(38) event: Ede stole the cookie. Bill saw it. proposition: Ede stole the cookie. Bill knows it. speech act: A: Ede stole the cookie. B: That’s a lie!
The referents of these di ferent anaphors are speci ed below.
(39) [ActP ASSERT [TP Ede steal-PAST [vP tEde tsteal the cookie]]]
dspeechact d’prop d”event
For the purposes of putting forward a proposal concerning the usage of polar response parti- cles, Kri a o fers two critical points: polar response particles are anaphoric, and utterances create more than one referent. Therefore, building on this framework that Kri a has developed, a polar response particle should be able to target any of the available referents of an utterance. Not only is there no evidence that a response particle must respond to the at-issue content, in fact there is evidence that a response particle can target the same components identi ed in (38).
46 One question, however, is whether the list of referents that Kri a puts forward is necessarily exhaustive, or whether an utterance could contain other potential referents, such as speaker or hearer belief. Consider the following example, overheard in a restaurant.
(40) Context: A couple entered a local restaurant, and their body language and facial expressions su ested that they had been fighting. They were seated at a booth, and the waitress brought them each a glass of water. The woman drank from a glass. Man: You totally just drank from my water! Woman: Yeah. I did NOT just drink from your water!
The proposition in the man’s utterance is clear, that the woman drank from a glass of water which belonged to him. The at-issue content is the same, that the woman drank from a glass of water which belonged to the man. Neither party seems to dispute that the woman took a drink; as they were both present at the event, this should be in their common ground. Both people had their own glasses of water, so the presupposition triggered by the man’s use of the pronoun ‘my’ stands. The accusation is that she drank from h water instead of her own. The semantics of these three potential referents (event, proposition, speech act) is similar to that in (39).
(41) Utterance: You totally just drank my water!
event: [vP twoman tdrink water]
at-issue content (p): [TP you drink-PAST [vP twoman tdrink water]]
speech act: [ActP ASSERT[TP you drink-PAST [vP twoman tdrink water]]] belief in p: x commits to the truth of p, where p=y drank x’s water QUD: {p,¬p}, where p=y drank x’s water
The man’s utterance, ‘You totally just drank my water!’ introduces all of these components, including the original three (event, at-issue content and speech act) and the belief and QUD. The response particle in woman’s response, ‘yeah’, selects one of these as its target. Because the response is ‘yeah’, it signals agreement to something, but because both the target of ‘yeah’ and the followup content must simultaneously enter the common ground, the target cannot contradict the followup content, ‘I did not just drink your water.’ This rules out the event, the at-issue content, and the speeech act of assertion, all of which require that the at-issue content be true. But the belief state, where x believes that y drank x’s water, does not require the at-issue content to be true. The woman’s ‘yeah’ response can signal acceptance of the man’s belief in the at-issue content while the followup content can reject the truth of the at-issue content.
47 Table 2.2: Polarity features of response particles
Absolute Polarity [+] non-negated prejacent [−] negated prejacent Relative Polarity [agree] preserves polarity [reverse] reverses polarity
Crucially, under the Response Target Hypothesis, response particles and followup can have di ferent anaphors, which is an essential property for understanding responses like ‘no, it’s true.’ This is an advantage over the ellipsis approach, under which paradoxical responses should be impossible. By adopting an anaphoric approach, the response particles and the followup content are able to respond to di ferent components of an utterance, allowing one response component to accept one utterance component, and the other response component to reject something else from the utterance.
Response polarity
In addition to the anaphoric dimension, response particles have annother important prop- erty which determines how they contribute to the common ground, namely that of polarity. The triggering utterance and the response followup content also have polarity, and the alignment of these elements’ polarity contributes to common ground management. This section will look at the polarity of response particles, speci cally using the polarity fea- tures in Table 2.2. Polarity exists in two forms, absolute and relative. Absolute polarity, under Roelofsen and Farkas (2015), is a component of the prejacent clause which is then ascribed to the particle, while relative polarity re ects the agreement of the response particle to the preceding utterance. Table 2.3 presents the response particles and their polarities in English and Romanian. The table highlights the con ation of di ferent properties in English particles that does not hold in Romanian; it provides for properties of these response particles, rather than a de nition. This separation between absolute and relative polarity can help explain things like English answers to questions with negation, such as in (42). Where the triggering utterance does not include negation, only one response is available to instantiate the appropriate combination of relative and absolute polarity.
48 Table 2.3: Polar response particles in English and Romanian
English [agree] and [+] can be realized by ‘yes’ [reverse] and [–] can be realized by ‘no’ Romanian ‘da’ can realize [+] ‘nu’ can realize [–] ‘ba’ can realize [reverse]
(42) a. Peter called. Yes he did. \ *No he did. [agree,+] b. Peter called. *Yes he didn’t \No he didn’t. [reverse,−] c. Peter didn’t call. Yes he didn’t. \ No he didn’t. [agree,−] d. Peter didn’t call. Yes he did. \ No he did. [reverse,+] (Roelofsen and Farkas, 2015, p. 32)
Importantly, the relative polarity of both responses in (42c) is [agree]. The relative polarity is identical between the two particles because they both signal agreement with the p on the lef . Both responses also have the identical absolute polarity, because they derive this polarity from the prejacent. This is also why the responses in (42d) have the same absolute and relative polarity. It follows from Roelofsen and Farkas’s system of the realization of polarity features by re- sponse particles that in (42c,d) either response particle is possible. In the case of (42c), ‘yes’ is licensed because it is realizing the relative polarity feature [agree] and ‘no’ is licensed because it is realizing the absolute polarity feature [−]. In (42d), ‘yes’ is licensed because it is realizing the positive absolute polarity feature [+], and ‘no’ is licensed because it is realizing the relative polarity feature [reverse]. The question which emerges concerns the extent to which these polarity features account for the distribution of paradoxical responses and their interpretation. Crucially, Roelofsen and Farkas (2015) predict that paradoxical responses are ungrammatical when responding to utter- ances without negation, but the data disprove this prediction. One other crucial point is that their analysis builds on ‘yes’ and not ‘yeah’, but these are not the same in their uses and distribution (Pope, 1972; Wiltschko, 2016). This is part of what obscures the grammaticality of paradoxical responses, as (43) demonstrates.
(43) a. Peter called. *Yes he didn’t. Yeah he didn’t.
49 2.2.2.3 Adopting a view of response particles
The consensus among all accounts is that, at their core, ‘yeah’ is an agreeing particle and ‘no’ is a rejecting particle, and there is no evidence to suggest that this is incorrect. In fact, looking at base uses of ‘yeah’ and ‘no’, with no followup content or other particles, their agreeing and disagreeing properties are di cult to argue with. The contribution of the Response Target Hypothesis is that ‘yeah’ can agree to the inclusion of semantic content beyond the at-issue content into the common ground, while ‘no’ can dis- agree to the inclusion of semantic content beyond the at-issue content into the common ground. In short, both response particles have a greater range of potential targets, and both particles can include (or exclude) most of those targets into the common ground. The Response Target Hy- pothesis draws inspiration from Kri a (2013) and adopts the polarity system of Roelofsen and Farkas (2015), and it suggests that responses can target several components that are introduced by an utterance, namely at-issue content, QUD, and beliefs and commitments. If ‘yeah’ is targeting, for instance, a speaker belief, then all else being equal, that ‘yeah’ is agreeing to, or accepting, that belief into the common ground.7 Thus the view of response particles that I adopt is not really a departure from that of other scholars, but given the limited work on paradoxical responses, my goal is to account for why response particles can be used in instances where the intended meaning of the response seemingly disagrees with the core function of the response particle. The conclusion that I come to is that the followup content has a signi cant e fect on how response particles contribute to the management of the common ground. My analysis makes predictions about what can and cannot be included in the common ground beyond the at-issue content: ‘yeah’ can accept belief (‘I know you think that, but…’) and the QUD (‘That’s a good question, to which I have no answer…’), and ‘no’ can reject the QUD as invalid (‘We don’t need to discuss that, because there’s already an answer…’) based on the answer already being in the common ground. 7A note about terminology, speci cally ‘(dis)agree,’ ‘accept,’ and ‘reject.’ It is important to remember that these are all terms that re ect the action of a given response component (a response particle or additional content) toward the inclusion of something into the common ground as part of the Acceptance phase of discourse. Clark (Clark and Brennan, 1991; Clark and Marshall, 1978; Clark and Wilkes-Gibbs, 1986) uses the terminology of accept and reject, whereas, Roelofsen and Farkas (2015) use agree and reject. The experiment that I discuss in Ch. 4 uses agree and disagree, and in the name of consistency I will endeavor to use those terms unless I am citing or discussing speci c other work.
50 2.2.2.4 Modeling the response
The sample discourse used for tracking contains several responses; in fact every utterance except the rst one is a response to something.
(44) Sample discourse for tracking information—Af er the response 0. No speaker; Z is planning her contribution to the discourse 1. Z: I’m excited about changing schools from Handsworth to Argyle. 2. E: Yeah, that’ll be a good change for you. And it’s so much closer. 3. Z: No, it really is. 4. E: But Southerland is a good school, too. 5. Z: No, it’s not, it’s full of drug dealers.
The response contains a response particle and followup content. The response particle ‘yeah’ has positive absolute polarity, which derives from the polarity of the triggering utterance in line 1. The followup content begins with “that,” an anaphor referring to “changing schools” in line 1. This is reinforced by the predicate which refers again to “a good change.” The evidence of the followup content demonstrates that the response in line 2 has the relative polarity of [agree]. In line 5, ‘no’ is realizing [reject,−], because the at-issue content is being rejected, or disagreeing, and the prejacent contains negation and is therefore [−]. The response in line 3 is the paradox which the Response Target Hypothesis is intended to explain. The triggering utterance has a positive polarity, the response has a relative polarity of [agree], at least based on the content “it really is.” The followup content targets the at-issue content and agrees to its inclusion in the common ground (for empirical evidence to support this claim, see the experimental results in Ch. 4), leaving ‘no’ to target something else. It cannot target the at-issue content without creating a paradox. But it can signal disagreement with the QUD based on the assumption that community membership (the same school district) has al- ready placed an answer in the common ground. Therefore, ‘no’ should signal either [reject] or [−] to at least one target. The alternative would be that the response in (44) line 3 has the same [agree,+] polarity of the responses in (42), which would amount to saying that any response can have any polarity combination. Such an open ended theory would not o fer much predictive power. The remedy is the Response Target Hypothesis, in which relative polarity can be relative to di ferent targets in di ferent contexts. Consider the example in (45).
51 (45) Utterance: α: I don’t care what you say, there are Martians living in LA! at-issue content: There are Martians living in LA. Belief: Bel(α)(p = ∃x Martians(x) and lives in LA(x)) Response: β: Yeah, there are no Martians living in LA! absolute polarity: [−] relative polarity: Bel(α)(p): [agree] β agrees to the belief.
The response particle is targeting the belief for acceptance into the common ground, because it cannot be targeting the at-issue content. The followup content is incompatible with the at- issue content from the triggering utterance. The contents of the common ground will be changed af er (45); the following section will look at how the common ground is updated for both speakers and what this means for the next phase of the discourse.
2.3 After the exchange
This section will look at how the common ground can be impacted by an exchange and how speakers process these updates in memory. It will take the previous information about the pre- sentation and acceptance phases of discourse, and look at the consequences of that exchange on the common ground.
2.3.1 Updating the common ground
The previous sections looked at the two step process of updating the common ground and the role of response particles in that process. Fig. 2.5 allows us to visualize this process so that we may consider its various stages and processes in relation to each other. The polarity of the triggering utterance, and whether that polarity matches that of the re- sponse, matters in updating the common ground, both in terms of how relative polarity targets the at-issue content and in the other potential targets available for responses and followup con- tent. This is why updating the common ground is a separate node from the response itself; the response does not on its own update the common ground. The crucial feature of response par- ticles that allows them to select targets other than the at-issue content is their relative polarity. By referring to the diagram in Fig. 2.5 and carefully exploring each path, the interaction of these features will become clear.
52 YES CG+p
CG Utterance Response Update CG? CG’
CG+∅ NO
Figure 2.5: Updating the common ground (or not) during discourse
Where the utterance has [agree] polarity, as in (46), it is clear that the at-issue content in the triggering utterance will be entered into the common ground.
(46) a. Speaker K: Julia has a really cute dog. absolute [+] polarity b. Speaker L: Yeah, she does. absolute [+] and relative [agree] polarity c. OUTCOME: Common ground updated (CG + pJ has a really cute dog = CG’)
This contrasts with (47), in which the relative polarity is [reject] and the response followup content has absolute polarity of [−].
(47) a. Speaker K: Julia has a really cute dog. absolute [+] polarity b. Speaker L: No, she doesn’t. absolute [−], relative [reject] polarity c. OUTCOME: Common ground not updated (CG + ∅ = CG’)
This is not to say that the at-issue content introduced by Speaker K, that Julia has a really cute dog, can never be placed in the common ground; it simply means that the information is not placed in the common ground as a result of this exchange. In the terminology of Farkas and Bruce (2010), the negative response in (47) puts the conversation in “crisis”, constraining the discourse until the questions of Julia’s ownership of a cute dog can be resolved. The key point here is that this resolution is the work of other pairs of utterances and responses. In considering Fig. 2.5 again, the importance of separating the process of update from the response particles becomes clearer in (48) and (49). Both of the triggering utterances have an absolute negative polarity, and both ‘yeah’ responses have an absolute positive polarity. But one exchange (48) does update the common ground, while the other, (49), does not. The di ference is the relative polarity.
53 (48) a. Speaker K: Julia doesn’t have a dog. absolute [−] polarity
b. Speaker L: Yeah, she does elided(have a dog). absolute [+], relative [reject] polarity c. OUTCOME: Common ground not updated (CG + ∅ = CG’)
(49) a. Speaker K: Julia doesn’t have a dog. absolute [−] polarity
b. Speaker L: Yeah, I know elided(that she doesn’t have a dog). absolute [−] polarity, relative [agree] polarity c. OUTCOME: Common ground updated (CG + pJ doesnothave a dog = CG’)
The crucial di ference between (48) and (49), is the relative polarity of the responses. When the relative polarity of the response is positive, the common ground is updated, as in (49), and where the relative polarity is negative, as in (48), the common ground is not updated. The relative polarity in these cases is signalling the agreement of the interlocutor to the update of the common ground. Thus it becomes possible to update the diagram of common ground update to that in Fig. 2.11.
YES: relative [agr] CG+p Response: Utterance: absolute CG absolute [+/−]& Update CG? CG’ [+/−] relative [agr/rej] CG+∅ NO: relative [rej]
Figure 2.6: Updating the common ground (or not) during discourse, with polarity
What happens if the response is ‘no’, as in (50) and (51)?
(50) a. Speaker K: Julia doesn’t have a dog. absolute [−] polarity
b. Speaker L: No, she doesn’t elided(have a dog). absolute [−] polarity, relative [agree] polarity c. OUTCOME: Common ground updated (CG + p = CG’)
54 (51) a. Speaker K: Julia doesn’t have a dog. absolute [−] polarity
b. Speaker L: No, she does elided(have a dog). absolute [+] and relative [reject] polarity c. OUTCOME: Common ground not updated (CG + ∅ = CG’)
Example (50) has a relative polarity of [agree], in that the response signals agreement with the triggering utterance. Therefore the common ground gets updated with the at-issue content asserted in that utterance. This contrasts with (51), in which the relative polarity is [reject], sig- nalling that the common ground should not be updated with the triggering utterance. The process of updating the common ground is dynamic and tractable, in that the speaker and addressee both make their contributions, and then they assess what was said to know whether the common ground is updated or not. It is also iterative, because the state of the common ground af er the exchange, CG’ in Fig. 2.12 becomes the basis for the next update cycle; it be- comes the state of the common ground before the next part of the conversation.
YES: relative [agr] CG+p Response: Utterance: absolute CG absolute [+/−]& Update CG? CG’ [+/−] relative [agr/rej] CG+∅ NO: relative [rej]
CG’ becomes CG
Figure 2.7: Updating the common ground (or not) during discourse, with polarity and iteration
The key takeaway is that, despite the strengths of the Roelofsen and Farkas (2015) analysis, it cannot account for paradoxical responses. It will either undergenerate by predicting that para- doxical responses are ungrammatical (they are not), or it will overgenerate by allowing all polarity combinations to occur in essentially free variation. By providing a richer set of targets for response polarities, the Response Target Hypothesis addresses this shortcoming. The next section will look at the theoretical models that are intended to capture the facts of this process, particularly the Table model (Farkas and Bruce, 2010), and Commitment Space Se- mantics (Kri a, 2015). These models provide the tools necessary to bring together the process of the common ground detailed above with the Presentation and Acceptance phase of discourse
55 presented in Sec. 2.2, the QUD, at-issue content and beliefs described in Sec. 2.2.1, and the prop- erties of response particles and the common ground in Sec. 2.2.1.
2.3.2 Models of discourse
By now all of the components of discourse and common ground management have been de ned and described. At this point the question turns to pulling all of these disparate components together into a single system that can illuminate the process of discourse based common ground update through the use of response particles. Two models will be discussed below, both of which respond to the question of how this whole process works. The rst will be the Table model of Farkas and Bruce (2010), which proposes that contributions go on a Table and are resolved from there. The second is the Commitment Space model of Kri a (2015), which proposes a process by which utterances and their components are added (or not) to the common ground, or commitment space, incrementally. Both models o fer valuable tools which contribute to understanding the process of discourse.
2.3.2.1 The Table
The Table model represents the ow of information from the initial assertion through to the common ground, by way of the Table and the Stack. The Table contains “items that are still under discussion” (Farkas & Bruce, 2010 p. 89) and is represented as a Stack, in that these items are “stacked” so that only the topmost item is accessible.8 In their model, information begins as a discourse commitment of a participant (A or B in Fig. 2.8) which is conveyed by the utterance that an individual speaker publicly states, which creates the projected set, that is “the set of future common grounds relative to which the issue on the Table is decided.” (Farkas and Bruce, 2010, p. 87). The Farkas and Bruce (2010) framework was developed to represent propositions through this process, and response particles are speci cally assumed to target only propositions, to ei- ther accept or reject the proposition at the top of the Stack’s inclusion into the common ground. Moreover, the model was designed around fairly simple adjacency pairs such as (52).
8Kim Bruce is a computer scientist by training, and the Stack is likely derived from the term ‘stack’ as it is used in object oriented programming, that is a vertical assembly of data in which only the topmost element is accessible and must be resolved before accessing what is underneath (this is called “popping” the stack in computer science, though Farkas & Bruce (2010) have modi ed this terminology to be “popping the top” of the stack).
56 A Table B
DCA S DCB Common Ground cg Projected Set ps
Figure 2.8: Sample Context Structure from Farkas & Bruce
A Table B
DCA p p=Sam home. DCB Common Ground cg Projected Set ps{cg∪ p/¬p/∅ }
Figure 2.9: Sample Context Structure from Farkas & Bruce af er utterance is introduced The proposition p is placed on the Table, and the placeholder p is placed in A’s discourse commitments.
(52) A: Sam is home. This utterance leads to the Table in Fig. 2.9 B: Yes, Sam is home./ No, Sam isn’t home. adapted from (Farkas and Bruce, 2010, p. 82)
In this example, A’s utterance asserts a proposition, that Sam is home, and B’s response in- cludes a response particle and followup content which agrees with the response particle. That is to say that the followup content af er ‘yes’ signals agreement with the proposition, and the con- tent af er ‘no’ signals disagreement. In terms of polarity, ‘Yes, Sam is home.’ is [+,agree], and ‘No, Sam isn’t home.’ is [−,reject]. Under the Tableframework, Speaker A makes a discourse commitment ‘Sam is home,’ which goes in the cell labeled DCA (or A’s Discourse Commitment), and it goes on top of the Stack, la- beled S, on the Table; in Fig. 2.9, the proposition p goes in two cells, DCA and the Stack on the Table. At that point the common ground is unchanged, but the projected set includes a few possibilities, as indicated in Figure 2.9; the common ground can either be updated with p, ¬p, or nothing. Moreover, the addition of p to the Table makes it possible for B to then act on the proposition introduced by A by accepting it or rejecting it. If B accepts the proposition intro- duced in DCA, then the common ground is updated to include the new information. If B rejects the proposition, then the common ground is not updated, and a “crisis” is created. That crisis might be resolved by further discourse, which will involve the addition of other propositions, but crucially, the common ground will not be updated with the proposition in DCA at that point in the discourse. There are two potential outcomes from this exchange:
57 1. The proposition “Sam is home” is asserted as being true by A and accepted as being true by B, and therefore the common ground is expanded by the inclusion of this proposition. 2. The proposition “Sam is home” is asserted as being true by A and rejected as being not true by B, and therefore the common ground is not expanded and the conversation is in crisis.
The exchange in (52) can only resolve the Projected Set in one way: ”Yes, Sam is home” selects {cg ∪ p} as the updated common ground. Otherwise ”No, Sam isn’t home” leaves the Table unchanged while the matter is decided in further discourse In contrast to (52), (53) presents a paradoxical response. Under the Table model, the response particle should create a crisis, but the followup content is consistent with cg∪ p.
(53) α: I think we need to be meeting at least once a month. β: No, you’re right.
When β says, “No, I think you’re right,” what are they signaling? Is ‘no’ leaving the Table unchanged, as in (52)? Or is it selecting p for inclusion, based on “you’re right.”? Intuitively, we understand the response as signalling support for the suggestion that the group meet at least once a month. The problem is, how do we model that? While the Farkas & Bruce model captures that expanding the common ground is a mutual process, it does not seem able to account for paradoxical—but entirely natural—responses. The strength of the Table model is that it creates a system to model the ow of information from utterance to common ground. It is a theoretical model that accounts for the data by for- malizing the relationship between an utterance and the common ground. By creating a Table to capture the proposition introduced in an utterance, the model also creates a link between the response and the update of the common ground. Moreover, the recognition that ‘no’ does not necessarily place ¬p into the common ground is important, because it reinforces the idea that common ground is mutual. The Table model allows this disagreement to be resolved by letting the discourse continue, using the same Table model to capture subsequent turns, but crucially the Tabledoes not allow the exchange to be resolved at that point. However, under the Response Target Hypothesis, (53) can be resolved. Thus the table provides an elegant model on which to build, but it needs supplementing with the Response Target Hypothesis.
2.3.2.2 Commitment Spaces
Kri a’s commitment space model (Kri a, 2014, 2015) sets out to capture the semantic informa- tion of an utterance as well as to track discourse, including which speaker commits to which
58 information. It relies on two unique stages: the commitment state, which is the set of proposi- tions c (and not possible worlds) that are non-contradictory to what is in the common ground; and Commitment Spaces C, which is the set of commitment states that represent the future de- velopment of the common ground (Kri a, 2015, p. 329). Let us look at each of these stages in turn. Commitment states represent what one individual has publicly committed to believing. An assertion takes the notational form of (54.1), in which S1 is the speaker of the utterance, ⊢ is that speaker’s commitment to the truth of φ, and φ is the proposition of the utterance. This results in the proposition becoming part of the commitment state. The commitment state is updated to (54.2). Importantly, commitment states are non-contradictory, i.e. they cannot contain both p and ¬p.
(54) Commitment States (c)
1. Assertion: S1 ⊢ φ
2. Commitment state c af er assertion: { S1 ⊢ φ ∈ c }
Commitment spaces take this assertion and map all of the possible future commitment states, which is notated as in (55). At the time of assertion, S1 publicly commits to the truth of a propo- sition φ, which is depicted in (55.1). Assuming that the interlocutor, S2, accepts the truth of φ based on the evidence of S1’s belief, C is then updated to (55.3), indicating that the Commitment
Space includes φ and S1’s commitment to the truth of φ. If S2 signals that they do not commit to the truth of φ, then a retraction is required, which is characterized in (55.4).
(55) Commitment Spaces (C)
1. S1 commits to truth of φ: {c ∈ C | S1 ⊢ φ ∈ c}
2. S2 acknowledges S1’s commitment: {c ∈ C | S1 ⊢ φ ∈ c ∧ φ ∈ c}
S2 3. S2 agrees with φ: {c ∈ C | S1 ⊢ φ ∈ c ∧ φ ∈ c} + [S2 ⊢ φ]
S2 4. S2 rejects φ: {c ∈ C | S1 ⊢ φ ∈ c} + ℜ + [S2 ⊢ φ] (Kri a, 2015, p. 334-335)
Kri a helpfully includes schematics of these various progressions, reproduced in Fig. 2.10, which correspond to the representations in (55); these visuals help to demonstrate what infor- mation is contained in the commitment space and under what conditions. In Fig. 2.10a, the
Commitment Space includes S1’s commitment to φ (in the lightly shaded box) and S2’s acknowl- edgement of this commitment in the innermost box. To put this in comparison with Clark and
59 (a) acknowledgement (55.2) (b) agreement (55.3) (c) rejection (55.4)
Figure 2.10: Diagrams of Commitment Spaces in (55), from Kri a (2015, p. 334)
Brennan (1991), this Commitment Space is only slightly more advanced than the presentation phase, but it does not approach the level of the acceptance phase. Acceptance occurs in Fig. 2.10b, in which the innermost box includes S2’scommitment to φ as well as S1’scommitment to φ in the outermost shaded box. Fig. 2.10c also proceeds from the acknowledgement state, representing
S2’s rejection of φ and, by extension, their commitment to ¬φ, in the innermost shaded box. Im- portantly, however, S1’s commitment persists in this commitment space, regardless of the truth of φ, as indicated by the dashed-line rectangle in the lower lef . Even this brief sketch of Kri a’s Commitment Space Semantics presents properties that are relevant for the hypothesis in this dissertation, including the persistence of one speaker’s commit- ment to the truth of φ regardless of the other speaker’s agreement. Figure 2.10c and (55.4) provide a mechanism whereby S2 can disagree with φ introduced by S1 without completely removing φ from the realm of shared information between the two speakers. If, as I argue in this dissertation, responses can be used to simultaneously accept a speaker’s belief in at-issue content and reject the truth of the at-issue content, then any model of shared information must be able to capture both of these properties simultaneously. Kri a’s Commitment Space model seems able to do just that. The model assumes a one-to-one correlation between response particles and their impact on the common ground. For example, in his discussion of what I have labeled Fig. 2.10, Kri a says the rightmost image corresponds to ‘no’, and the middle one corresponds to ‘yes’, and by extension ‘yeah’. But the previous sections of this chapter have provided numerous examples where the correlation, of ‘no’=rejection of the at-issue content and ‘yeah’=agreement, does not hold. Therefore, while I will adopt Kri a’s Commitment Space model to a large extent, I will allow for a loosening of the relation between the response particle and how the common ground is updated.
60 2.3.3 Role of memory
The previous discussions on the nature and update of the common ground discussed many of the mechanics of common ground use in discourse, but there are some important questions lef unanswered. Where is the common ground stored? Why is it impossible to be absolutely certain of what is in the common ground? Such questions speak to the role of memory in common ground management. Common ground is conceived of as a repository of shared information, and the only possible place for this repository to be stored is in the minds of interlocutors. Where physical copresence is the source of common ground, interlocutors may rely on their senses to update the common ground. For community membership and discourse-created common ground, the information must be stored in the memory of individuals. If utterances were stored verbatim, then this would have implications for the common ground, as systems such as Kri a’sand Farkas & Bruce’swould not need to track discourse commitments; it would come “for free” as part of speaker memory.9 The reason for this is that, if each utterance is stored verbatim in memory, that is if speakers have available in their memory an exact transcript of the previous discourse, then there is no need to model why the common ground captures semantic information without preserving its syntactic shape; both the shape and the information would simply be functions of memory. However, ex- tensive and long-standing research has shown that this is untrue, that rather than storing a word- for-word transcript of discourse, interlocutors retain the gist of a discourse in their memory, an e fect which holds true for both short- and long-term memory retention (Sachs, 1967; Anderson, 1974; Reyna and Kiernan, 1994, and works cited therein, among many others). Others take a more nuanced view, demonstrating that some utterances may be stored verbatim in limited cir- cumstances (Keenan et al., 1977; Reyna and Kiernan, 1994; Gurevitch et al., 2010, among others). Sachs (1967) showed in her seminal dissertation that, rather than storing the verbatim con- tent of an utterance, interlocutors stored only the gist of what was said; in other words, mem- ory records the semantic content and not the syntactic form. Her conclusions found support in Anderson (1974), who found that the verbatim form of a sentence may be stored in short term memory, but when participants’ long term memory was tested, they could con rm the veracity of a sentence, but not the form of the sentence. Reyna and Kiernan (1994) showed that the inter- vention of even one other sentence could be su cient to degrade a speaker’s short term verbatim recollection of a sentence, but that the semantic content could be retained over much longer pe- 9That discourse is stored verbatim seems to be an assumption ascribed to the eld of conversation analysis (Sche- glo f, 1968; Sacks et al., 1974), but this is neither a claim nor a question for conversation analysis, which instead focuses on turn-taking, overlap, and other factors concerned with the process of conversation, not necessarily the output.
61 riods. These and other works seem to o fer exceptionally strong evidence that semantic content, and not syntactic constructions, are stored in memory; this seems to work nicely with the idea that the common ground is the repository of propositions, which are by de nition semantic in nature. As speakers of language though, we have the experience of remembering an utterance ver- batim, and we might therefore wonder how that impacts the common ground. A body of lit- erature within psycholinguistics provides some nuance to the claim that verbatim utterances are never stored in memory by demonstrating that there are conditions under which verbatim ut- terances are stored in memory, speci cally when listeners are instructed to remember or when there is some personal value attached to words. Johnson-Laird and Stevenson (1970) were among the rst to demonstrate that syntactic constructions were not stored in memory, but their later work showed that, if participants were warned that they would be tested on their retention of sentences, participants were in fact able to store sentences in their memory; Reyna and Kiernan (1994) also found that participants’ recall ability was increased if they knew beforehand that they would be tested on certain sentences. Without a reliance on verbatim memory, the common ground becomes an important tool in modeling the exchange of information between interlocutors. Models such as Kri a’s Com- mitment Space provide a mechanism for how speakers are able to manage several components which are introduced by a single utterance. Relying on memory to capture the gist of an utter- ance without preserving its shape allows us to store the information in the common ground, so that ‘p is true’ becomes ‘α and I both know that p is true.’
2.3.4 Modelling common ground update
The sample discourse used throughout this chapter includes both possible outcomes of common ground update: updated and not updated. The response in line 2 accepts the at-issue content introduced in line 1, which adds the at-issue content to the common ground.
62 (56) Sample discourse for tracking information—Af er the response 0. No speaker; Z is planning her contribution to the discourse 1. Z: I’m excited about changing schools from Handsworth to Argyle. 2. E: Yeah, that’ll be a good change for you. And it’s so much closer. 3. Z: No, it really is. 4. E: But Southerland is a good school, too. 5. Z: No, it’s not, it’s full of drug dealers.
The common ground at the very least contains information regarding the context of the dis- course and that which results from shared community. This is the common ground that exists in line 0. Af er the response in line 2, the common ground is updated to include the at-issue content in line 1, as indicated in Fig 2.11.
YES CG0+paic1
2 CG0 Utterance1 Response Update CG? CG’
Figure 2.11: Updated common ground af er line 2
The sample discourse also includes a rejection of the at-issue content in a previous utterance, in which case the common ground is not updated. This is the result of lines 4 and 5; line 4 intro- duces the at-issue content that Southerland is a good school, and line 5 rejects this assertion.
(57) Sample discourse for tracking information—Af er the response 0. No speaker; Z is planning her contribution to the discourse 1. Z: I’m excited about changing schools from Handsworth to Argyle. 2. E: Yeah, that’ll be a good change for you. And it’s so much closer. 3. Z: No, it really is. 4. E: But Southerland is a good school, too. 5. Z: No, it’s not, it’s full of drug dealers.
63 Because there is no content that has been mutually agreed to for entry into the common ground, the common ground is not updated; CG” is identical to CG”’as re ected in Fig. 2.12.
5 CG” Utterance4 Response Update? CG”’
CG0+∅ NO
Figure 2.12: Updated common ground af er line 5
This is not to say that the at-issue content introduced in line 4, that Southerland is a good school, can never be introduced into the common ground. Subsequent turns can potentially establish that information in the common ground, once both speakers signal their acceptance of it as true. But crucially, this pair of utterances does not alter the common ground, because the negative response signals a lack of agreement to the inclusion of any information introduced by the utterance. The pairs that have been discussed thus far, lines 1 and 2 and lines 4 and 5, are clear instances of the responding speaker accepting or rejecting the at-issue content. What they say af er the re- sponse particle reinforces the response particle. The utterance pair of lines 2 and 3 is potentially ambiguous, because the response particle and the followup content of the utterance are not con- sistent with each other. ‘No’ seems to signal disagreement, while ‘it really is’ signals agreement. So which is it? (58) Sample discourse for tracking information—Af er the response 0. No speaker; Z is planning her contribution to the discourse 1. Z: I’m excited about changing schools from Handsworth to Argyle. 2. E: Yeah, that’ll be a good change for you. And it’s so much closer. 3. Z: No, it really is. 4. E: But Southerland is a good school, too. 5. Z: No, it’s not, it’s full of drug dealers.
As a speaker of English, my own intuition is that the response is agreeing with the assertion that the new school is closer. Examining both parts of the response in isolation supports this judgement, as (59) illustrates.
64 (59) a. E: It’s so much closer. Z: #No. b. E: It’s so much closer. Z: it really is.
A bare ‘no’ response, as in (59a), seems infelicitous to my intuition, as the location of the two schools and the location of Z’s home are both in the common ground (based on their shared community and prior history); this intuition is tested in similar discourses in Ch. 4. The response in (59b) ‘it really is’, signals agreement to the information in the triggering utterance, and Ch. 4 supports this as well. The Response Target Hypothesis o fers an answer to the question of why ‘no’ is being used in this example, when it is apparently paradoxical to the followup content. I argue that it dis- misses the QUD concerning the location of the school. This information is already in the com- mon ground because of the shared community membership of the speakers and their existing relationship, and therefore it is not a meaningful question under discussion. Therefore the im- pact of this pair of utterances on the common ground is one of con rming the presence of this information, rather than updating it to include something new.
2.4 Summary
This chapter broke a complex process into smaller components, but it is important to remem- ber that this process is constant and cumulative. Taking the sample discourse again as a model, it contains three adjacency pairs—assertions and responses—and takes less than 15 seconds for two speakers to complete. That means that these two speakers are considering their interlocu- tor, planning the utterance, delivering and interpreting the response, and updating the common ground in a matter of seconds, and then immediately repeating that process. Moreover, the process is managed with a versatile set of tools which interact with each other. Agreement is not signalled solely by the use of a “positive” response particle; a response which signals agreement in one context can signal disagreement in another. Because we cannot directly access the mental processes that speakers use in discourse, we must rely on the linguistic infor- mation in each utterance and response to support our understanding of the process. Important factors for consideration are the sources of pre-existing common ground; the polarity of the ut- terance and response, and whether those polarities match; the person(s) for whom the message is intended; the type of predicate; levels of evidence of agreement or attention. The following chapter will look at how evidence of these factors is found in corpora.
65 Chapter 3
Naturally occuring data
The Response Target Hypothesis claims that response particles in paradoxical responses can tar- get something other than the at-issue content. This chapter tests this claim through the use of corpora which make unscripted conversations available to use as data. Where corpora identify a broadcast source or an individual speaker, the time and setting are recoverable, and therefore available as part of the context for the analysis. In addition to the data from corpora, a few additional examples are taken from my own experience and observation; as a native English speaker living in an English speaking community, I am positioned to observe natural exchanges in natural settings. This chapter begins by discussing the methodology for processing corpus data (Sec. 3.1), including how tokens were selected for inclusion and a summary of the patterns that emerged from examining responses within the corpora (Sec. 3.2). The balance of the chapter is devoted to presenting data which tests the hypothesis (Secs. 3.3–3.5). The nal section summarizes the ndings of this inquiry.
3.1 Methodology
The use of paradoxical responses is a discourse phenomenon, and it is important to use unscripted discourse to examine the phenomenon in situ. Cataloging instances from listening to broadcast sources and observation does yield results, but not enough to draw robust conclusions. With these constraints in mind, corpus data was used to identify a su cient quantity of examples from which I might be able to discern patterns of use to support (or challenge) the Response Target Hypothesis. Selecting a set of tokens for further analysis, almost as a mini-corpus, is atypical of corpus
66 studies, which are usually driven by a quantitative analysis, perhaps of word frequencies or de- mographic information. For formal semantic analysis, one reason why corpus data is not typically used as the exclusive source of data is the perceived risk of “mindreading,” in which assumptions about the speakers’ viewpoints and knowledge are projected onto the conversation and used to explain the speakers’ motivations. To mitigate this possible problem, the analysis will focus on what is linguistically present in the conversation coupled with what is broadly available in the common ground of North American cultural knowledge.1 This section will walk through the process of analyzing the tokens from corpora, beginning with the selection criteria. It will then consider the criteria for determining whether the followup content of a response signals agreement or disagreement, and the criteria for determining the Question Under Discussion (QUD). An overview of the whole process will conclude this section.
3.1.1 Selecting the data (corpus and “wild”)
Three corpora were used as sources for data:
(1) Corpora used in analysis (i) Linguistic Data Consortium: – Fisher Corpus (Cieri et al., 2004) – Switchboard Corpus (Gra f et al., 2001) (ii) Corpus of Contemporary American English, or COCA (Davies, 2008)
These three were chosen because they include unscripted, spoken English which has been transcribed and is searchable. The LDC corpora make the audio les available to researchers, while COCA annotates its data with the information necessary to search for a recording. The corpora all present transcriptions of spoken discourse, and they each have their own tran- scription conventions. I have kept the examples as faithful to the corpora as possible, meaning that I have not corrected misspellings, capitalization errors, false starts, or any other “errors” in the transcription. The only exception was to correct the spelling of names to avoid the impres- sion that the di ferent spellings might refer to di ferent individuals. In isolated cases, I removed a long section of false starts, overlaps or backckannels so as to focus on something speci c in the dialog; these cases are always noted with […]. 1For a discussion of the various sources of common ground, see Sec. 2.1.1.
67 The Fischer and Switchboard corpora consist of recorded phone calls between strangers who volunteered to be part of the corpus. The participants were given some suggested topics of con- versation, but they were not required to discuss anything in particular and were free to converse naturally. A strength of these corpora for this dissertation is that the preexisting common ground between the participants was minimal; they were usually from di ferent states and held di ferent occupations, so that there was no prior community membership. The common ground that ex- ists between them derives from their shared community membership as late 20th century North Americans. While they do sometimes discuss national events or politics, they also discuss more mundane topics where there can be little preexisting common ground, such as their pets, neigh- borhoods and eating habits. As the conversations were all recorded over the phone, they were restricted to being conversations between two individuals with no expectation of an audience or overhearers as part of the immediate conversation. Participants did know that the conversations were being recorded for inclusion in the corpus, but they were the only participants at the time of the conversation. The signi cance of this is that the common ground need only be concerned with those two individuals. The format also means that there was no physical source for common ground. Thus everything had to be negotiated linguistically over the phone. The full COCA includes novels, newspapers, magazines, scripted and unscripted television programs, talk radio and several other sources; the corpus comprises over half a billion words and continues to grow. I con ned my use of this corpus to unscripted spoken exchanges that were broadcast on television or talk radio. As these conversations were broadcast, they di fer from the LDC corpora in that there was an immediate audience beyond the discourse participants, sometimes including an in-studio audience and always including the listeners/viewers for whom the program was made. Most of the exchanges are interviews, and they range from formal, one- on-one, in-depth conversations between a host and a guest, to panel discussions on news or sports broadcasts, to the banter of multiple hosts with one or more guests. This corpus provides a wide range of registers and speakers, and the conversations are on widely accessible, contemporary topics; thus the pre-existing common ground for both discourse participants and audience is based on shared community membership, namely that of early 21st centry North Americans. As COCA is continually updated, many of the most recent examples concern the 2016 U.S. election, recent sporting events, prize winning novels, etc. from the recent past, approximately 2010 to 2018; I imposed no further restrictions on when the exchanges took place. Finally, some of the examples discussed below are from the wild, in that they are exchanges that I have personally encountered. Some were from broadcasts or podcasts that I listened to, others were overheard in a public place. Where they were broadcast, I have included a source if
68 possible, and where I knew the speakers, their identities have been anonymized; no examples were taken from private conversations for which the speakers could expect privacy. When I did hear particularly relevant examples, I wrote them down or otherwise recorded them immediately so as to retain the original wording and context; I did not use examples where there was a signi cant gap between when I heard them and when I recorded them. I have also not included these wild examples in tables in which instances of the various uses were tallied. These examples are included because they are either particularly strong examples of a phenomenon or are easier to extract from their original setting than corpus examples were; because some corpus examples rely on several minutes of preceding conversation, they were unwieldy as examples in this chapter. It is also important to recall that the corpora were used as a source for tokens of data, not as the primary data; in other words, no analysis was done on every instance of ‘yeah’ in the COCA, for example, such that changing the corpus would have changed the analysis. No analysis was done using the entire corpora, so that using additional tokens does not undermine a corpus based analysis. To constrain my search, I used the same template for corpora exchanges (and wild conversa- tions) as I used for the experimental design, rst introduced in Chapter 1 and repeated in (2). In limited instances I have included extra prior turns to provide necessary context; in cases where the context is developed over an extensive timeframe, I have summarized the relevant context prior to the example.
(2) Adjacency pair template α:. Utterance contains a proposition consisting of at-issue content and representing α’s belief and discourse commitment, and responding to an understood QUD β:. response particle ‘yeah’ or ‘no’, and followup content that agrees or disagrees with the at-issue content contained in α’s utterance
Speakers of en kept speaking af er the response particle and followup content, and I have included the rest of these turns, provided they were on the same topic as the original utterance. Where they switch topics, I have not included the new topic information. An example of this is in (3), where speaker A uses their turn to switch the topic of conversation away from obituaries and onto work; the light grey portion represents the topic switch and would not be included in my analysis.
69 Table 3.1: Percentage of included results from total search results (est.)
yeah no
COCA 21% 11% LDC (Fisher and Switchboard) 24% 10%
(3) Participants are discussing the people listed in obituari 1. B: well what i mean is do you if your [sic] in a strange city you certainly don’t know them 2. A: yeah that’s true what type of work are you in Switchboard Corpus
My procedure followed two steps, a corpus search and then a content-based analysis. In the rst step, I entered ‘yeah’ or ‘no’ into the search eld, and for COCA I con ned the search to the unscripted spoken portion of the corpus. From the results, I selected examples that t the template above and were not excluded by other criteria (exclusion criteria, and examples, are in Sec. 3.1.2 below). The results of the ‘yeah’ and ‘no’ searches numbered in the millions; I started at the beginning of the search results as presented by the default algorithm of the database and checked each result for inclusion or exclusion. Because ‘yeah’ and ‘no’ are used in a number of contexts in English, the percentage of instances which quali ed for this study was relatively small. While I did not keep an exact count of the excluded examples, the results were presented in pages of 50, and I tracked how many pages were needed to extract 100 examples of ‘yeah’ and 100 of ‘no’ from both the COCA and LCD corpora. Table 3.1 shows the percent of responses of my search within the results from each corpus that were included in my dataset to give an approximate percentage of included results out of total results. In other words, 21% of the search results that I examined for ‘yeah’, or 21% of 950 returned results (19 pages of results, 50 results per page) t the template for inclusion in my data set. The LDC results are from two corpora, the Fisher and Switchboard corpora, but they are classed together under the LCD, because they are both comprised of unscripted telephone calls between strangers. The response ‘no’ was excluded more frequently than ‘yeah’, because it of en appears as a modi er in other environments, such as those in (4) and (5).
(4) CHUCK TODD: No matter how you slice this thing, the GOP tax plan is going to be a big boom for the wealthy and speci cally the Trump family. COCA
70 (5) 1. SASAY: We appreciate you being with us. 2. VAUSE: No worries COCA
The central research question asks what goes into the common ground as a result of the use of response particles; the followup content which comes af er the response particle is an important indication of what, if anything, is added to the common ground. In considering the 200 ‘yeah’ and 200 ‘no’ examples extracted from the corpora, the rst step was to determine whether the followup content agreed or disagreed with the at-issue content of the preceding utterance; the goal was to determine whether the followup content on its own was su cient to place the at- issue content in the common ground. In order to test the Response Target Hypothesis, the analysis of the corpus data needed to be clear and consistent. Therefore I looked for explicit evidence to support my initial judgements. I also reviewed the data several times and had a research assistant review the data as well, to ensure consistent classi cation. In cases where the diagnostic criteria is inconclusive but where the judge- ments were clear, for example dialogs with indirect or suggestive language, I describe how the data is consistent with the nal classi cation by presenting additonal information and discussion. Conversation is necessarily messy, as mentioned previously, so it will be helpful to use a “messy” example to illustrate the process of analyzing tokens from conversations. Consider (6), which comes from a broadcast interview.
(6) 1. MEGYN KELLY: But I love that you want little girls to know too that this is available to them and – and – and boys, like, young people need to realize this is an option for young women. 2. JUDALINE CASSIDY: Yeah. that we did a disservice to our country, by the images we portray of tradespeople. COCA
The rst speaker, Megyn Kelly, utters a sentence that includes several clauses, each of which has the potential to introduce at-issue content, and presuppositions:
(7) Clauses in (6) line 1
1. MK loves that
2. ↰ JC wants
3. ↰ girls to know that 4. ↰ this (trade career) is available to them (girls)
71
5. and young people need to realize that 6. ↰ this (trades) is an option for young women.
In considering what a response might be targeting, my analysis begins with the last clause and its at-issue content, and not the at-issue content from other clauses within the utterance. This allows for a systematic examination of the data, motivated by the structure of discourse. By selecting the nal clause, in this example line 6 of (7), it becomes possible to see speci cally how the common ground is expanded, as in (8).
(8) at-issue content. Thistrades is an option for young women. CG’. CG ∪ at-issue content
JC’sresponse accepts the overall sentiment of MK’sutterance, but it is di cult to assess specif- ically what, if anything, from MK’s utterance has entered the common ground. The value of selecting the nal full clause as the response target is particularly clear in a case like (9),2 where Speaker A’s utterance contains several clauses that have little if any syntactic or semantic relationship to one another, as is clear in (10). The followup content in Speaker B’s utterance responds directly to the nal clause of the Speaker A’s utterance.
(9) 1. A: i worked for the TI plant in Abilene and and i sure enjoyed the Southwest it was really neat but uh that that’s an interesting concept that this is going to be your your permanent home or okay 2. B: yeah we we bought this land out here we’re going to we’re going to retire on it LCD
(10) Clauses in (9) (i) ISpeaker A worked for the TI [Texas Instruments] plant in Abilene [Texas] (ii) ISpeaker A sure enjoyed the Southwest (iii) ItSouthwest was really neat (iv) That? is an interesting concept (v) This? is going to be your permanent home
2The original transcription records “… concept that is this going…”; I transcribed it as “… concept that this is going…” based on the audio.
72 An exception to using the last clause of an utterance as the at-issue content is when the last clause is o fered as evidence, or in support of, a previous clause, such as in example (11). The nal clause begins with “Like,” suggesting that it is being o fered as evidence in support of a previous statement.
(11) 1. Tra c is just nuts in the rain! Like yesterday people were all panicking because of a little water on the road! 2. Yeah, it’s insane.
Two clauses are introduced in line 1 of (11), listed in (12). At-issue content in this case would be hypothesized to be ‘Tra c is insane when the weather is bad.’
(12) Clauses in (11) line 1
(i) Tra c is just nuts in the rain. (ii) ↰ Like yesterday people were panicking because of a little water on the road.
Because the second clause is o fered as evidence to support the rst clause, the rst clause contains the at-issue content that the second clause is supporting. This understanding is rein- forced with the response followup content, “it’s insane.” The pronoun ‘it’ refers to tra c, and not people, and ‘insane’ and ‘nuts’ are synonyms in colloquial speech. The other main exception to using the last clause as the source of the at-issue content is when the nal clause is an object of a previous clause, as in (13).
(13) 1. Dogs act like every person on Earth exists to pet them. 2. Yeah, that’s why we love them.
The matrix clause, “Dogs act like…” takes the second clause, “every person on earth exists to pet them,” as its object. The at-issue content in this example would derive from the matrix clause and would include the embedded clause, ‘Dogs act like all people exist to pet them.’ It would not be the nal clause. The response supports this analysis as well, as ‘them’ refers to dogs, not “every person on earth.” This becomes clear when the pronouns are spelled out in the response, as in (14).
(14) a. Yeah, that’s why we love dogs, because dogs act like every person on the earth exists to pet them. b. #Yeah, that’s why we love every person on earth, because every person on earth exists to pet them?.
73 Ambiguous examples such as (6) are present in the corpus data; the template and exclusions used to select the tokens have limited the number of ambiguous examples present. While the pri- mary focus is understanding the role that responses play in constructing common ground, these ambiguous examples are interesting in other ways, because, as in (6), their interpretation of en relies on information already established in the common ground, demonstrating the importance of the common ground as a repository for information.
3.1.2 What was excluded from my study
This section provides examples of the kinds of tokens that were excluded from the study de- spite appearing to t the template. These exclusions fall into seven categories: fragmented dis- course, missing context, broadcast genre conventions, direct questions, quizzes, backchanneling, and other syntactic uses.
Fragmented discourse In selecting the exchanges to include in this study, I looked for utter- ances that had content that could be expressed in a single paraphrased sentence. I did not include exchanges where the initial utterance might be said to be fragmented. This is not a term in general use in discourse analysis3, but I use it to refer to utterances such as that in (15).
(15) Fractured discourse 1. SHEINELLE JONES-: Because you wake up at – 2. JENNA BUSH HAGER-: See, I have – 3. AL ROKER: You don’t have water rings on your co fee table? 4. SHEINELLE JONES-: No. 5. AL ROKER: On your night stand? 6. SHEINELLE JONES-: You wake up and – because it’s not cold. 7. DYLAN DREYER-: Yeah, that’s true. COCA
Other than the nal response in line 7, no other utterance in (15) includes a complete, declar- ative sentence. Lines 3 and 4 are an adjacency pair in the form of a question and answer, but lines 1, 2, 5 and 6 are all partial utterances spoken by three di ferent speakers and from which no single paraphrased sentence can emerge to capture the content. Given this level of fragmenting, 3Of course discourse analysis refers to sentence fragments, but to the best of my knowledge, discourse itself is not generally referred to as ‘fragmented’.
74 it is di cult to say with any con dence what the response in line 7 (spoken by a fourth speaker) might be responding to. Examples like this are not included in my analysis.
Missing context Some discourses were missing context, so that although there were complete sentences, the lack of surrounding information made it di cult to say with any certainty what the responses were intending to respond to. A typical example is in 16, in which it is impossible to tell what the speakers are discussing. There are four speakers, and there is mention of prices in line 2, a backpack in line 4, and “the idea” in line 5. What that idea is, and how it relates to prices and backpacks, is unclear. Although line 5 does include ‘yeah’ and followup content, it comes immediately af er “Wow” and may be intended to respond to lines 2 or 3. In short, this example is missing too much information to draw any conclusions about usage, and it, and others like it, are excluded from the study.
(16) Missing Context 1. HODA KOTB: Yeah. 2. SAVANNAH GUTHRIE:That’s the price and pretend to like it. 3. CARSON DALY: I’ll take the backpack 4. AL ROKER: Wow. 5. SAVANNAH GUTHRIE: Yeah. I don’t know. But we asked our viewers at home what they thought of the idea. 6. HODA KOTB: What did they say? 7. SAVANNAH GUTHRIE:They’re split – 8. HODA KOTB: Oh. COCA
Broadcast genre conventions As most of the corpora examples in COCA are from the genre of broadcast interviews, they re ect certain conventions of the genre, and conventions of saluta- tions and break segues have been excluded from this study. An example of such a salutation sequence is in (17), in which a speaker is narrating a story in line 1, and the other two speakers, mindful of the time, respond in lines 2 and 3 in ways that are short and agreeable but invite no follow-up from the original speaker. Line 4 begins with a response particle and agreeing fol-
75 lowup, but the speaker immediately thanks the speaker of line 1 and addresses the audience with ‘you,’ directing them to further action,4 which essentially ends the discourse prematurely.
(17) Salutations 1. PETER VANSANT: …So prosecutors believe because she has some wealth that she is a ight risk. 2. VLADIMIR DUTHIERS: All right. 3. GAYLE KING: Once again I’m intrigued. Very good tease. 4. VLADIMIR DUTHIERS: Yeah, I’m intrigued. Peter, thank you very much. You can watch Peter’s full report, The Psychiatrist and the Sel e on 48 HOURS, that is tomorrow night at ten, nine Central right here on CBS. COCA
Direct questions Examples which deviated from the template were not included. This in- cludes responses that were responding to overt questions that take the form of an interrogative, such as in (18.2).
(18) Questions 1. PETER SAGAL: And Melania decorated the White House to look like a satanist sex club. Did you see this? 2. LUKE BURBANK: Yeah. Somebody needs to leave some Eggo Wa es behind one of those trees for Melania COCA
These excluded examples comprise a large portion of the corpora, especially COCA, and they were excluded immediately and were therefore not counted towards my target number of 100 of each response particle from each corpus source, for a total of 400 examples. These excluded conversations comprise the majority of exchanges and explain why in Table 3.1 over 75% of ‘yeah’ responses were excluded from COCA, for example.
Quizzes Another speci c type of use for ‘yeah’ and ‘no’ is in quiz-type discourses, in which Speaker A poses a question to the other speaker(s) to determine whether they know the correct answer. An example of a quiz-type discourse containing ‘yeah’ plus agreeing content is in (19).
4That ‘you’ refers to the audience is not apparent from the example text, but it is a convention of the genre of broadcast inverviews, which frequently conclude by thanking the guest and directing the audience to some speci c action.
76 (19) Quizzes 1. OPHIRA EISENBERG: This is your last clue. According to the Hass Avocado Board, in 2016, Americans were expected to eat 278 million avocados during the week of what major televised event? 2. JASON MRAZ: That would be the Super Bowl. 3. OPHIRA EISENBERG: Yeah, that’s right. Yep. COCA
Line 3 contains ‘yeah’ plus agreeing content, but given the context, the primary purpose of the response is not to enter the at-issue content in line 2 into the common ground, it is to con rm that the speaker of line 2 correctly answered the question posed in line 1. This will likely impact the common ground by including not only the at-issue content but also the fact that Mr. Mraz answered the question correctly.
Backchanneling Backchanneling is used to signal continued attention while another speaker has the oor, and it is common particularly in the Fisher and Switchboard corpora. In (20), Speaker A uses ‘yeah’ and ‘is that right’ as backchannels while Speaker B continues to speak.
(20) 1. B: i guess there’s the other side of the issue that uh there need to be facilities for pro- cessing all this stu f and a market for recyclable goods 2. A: yeah 3. B: and that was something they brought up on the show i listened to was uh you know there’s all kinds of you know recycled paper but you know nobody’s wanting to use it 4. A: is that right 5. B: you know how many people are using how many people do you know that use recycled paper in the o ce 6. A: yeah 7. B: you know you can you can buy your your Moore’s computer forms for your com- puter paper or whatever you can buy it with regular paper or recycled and i’ll guaran- tee you in my building i haven’t seen recycled paper yet 8. A: yeah 9. B: so at the same time we’re we’re doing all this e fort to throw it a way and recycle it we ought to be working on the other end and and paying the extra dime on on a dollar for that recycled stu f
77 10. A: i’ve always wondered why they haven’t taken some of these recycled plastics and made them into like uh plastic boards to build a house with instead of making it out of wood lumber make it out of out of plastic boards LDC
Other syntactic uses In addition to its use as a response particle, ‘no’ is also frequently used as a modi er, a use which was also excluded from the study. Examples of such use are collected in (21); this list is by no means exhaustive.
(21) no kidding no matter no show no joke no way no one
Though ‘yeah’ is not used as a modi er in the same way as ‘no’ is, it can nonetheless be ex- cluded on syntactic grounds, as in (22), where it serves as the subject of the sentence and is a direct quote of another speaker. It therefore does not re ect the speaker’s agreement or point of view.
(22) “Yeah” was all she said.
3.1.3 Distinguishing between agreeing and disagreeing content
A crucial part of assessing the corpus data is identifying whether followup content agrees or dis- agrees with the at-issue content of the triggering utterance, independent of the response particle. The clearest examples are (near) verbatim repititions of the at-issue content and claims that stand in direct opposition to the at-issue content. Using the example5 of (23) as the triggering utterance and (24) as examples of response followup content, the di ference between the two becomes clear.
(23) Utterance and at-issue content Utterance: Game of Thrones is the best show ever made. at-issue content: Game of Thrones is the best (television) show ever made. QUDDomain Goal: Is Game of Thrones the best television show ever made?
5The examples in this section are constructed so that I could create minimal sets based on tokens from the corpora. The value is in ensuring an apples to apples comparison with as little contextual variation as possible as the criteria for evaluating are explicated.
78 (24) Response followup content (what comes af er the response particle) Utterance: Game of Thron the best show ever made. Agreeing: It (Game of Thrones) really is the best show ever. Disagreeing: Game of Thrones is nowhere near the best show ever made.
The confound is that, in natural language, hundreds of other responses are possible that will not be so neatly categorized. In order to make this problem tractable, I looked to Clark and Brennan (1991) for guidance, determining that answers which took up the conversational thread, as it were, would be considered agreement, provided that agreement with the at-issue content is at least an implicature of the response, as in the examples in (25). Such a conversational move could respond to the same QUD as the at-issue content, as line 1 does, or it could o fer an explanation why the at-issue content is true, as in line 2. Something that presupposes the truth of the at-issue content in the triggering utterance would also signal agreement, as in line 3. Lines 4 and 5 also signal agreement, albeit weaker than in lines 1-3; line 4 uses a hedging modi er, and line 5 expresses acceptance of the claim, although not whole-hearted agreement.
(25) Followup content signalling agreement Triggering Utterance: Game of Thrones is the best show ever made. 1. take up the thread: Everything about it was just so good! 2. explanation for truth: Well the writing was so much better than anything else. 3. presuppose truth: Mad Men is a close second when it comes to general goodness. 4. modify scope: It kinda is. 5. evaluate and concede: I can see that.
Similarly, disagreeing, or more accurately lack of agreeing, followup content can take many forms as well, as shown in (26). A response can simply claim something di ferent, as in line 1, or it can constrain the truth of the at-issue content as in line 2. By supporting the narrower claim that Game of Thrones is the best fantasy show ever, the speaker declines to accept the truth of the at-issue content, that Game of Thrones is the best show ever, bar none. Line 3 displays a similar strategy of disagreement, where the response makes no comment on the greatness of the show in question, claiming only knowledge of its popularity. Similarly, line 4 is ambivalent in that, if being “very adult” is considered a good quality then it might signal agreement, but if not then the comment signals disagreement. Line 5 fails to signal agreement by simply declining to commit.
79 These responses are insu cient to demonstrate that both interlocutors have signalled agreement to the truth of the at-issue content.
(26) Followup content signalling lack of agreement Triggering Utterance: Game of Thrones is the best show ever made. 1. di ferent claim: M*A*S*H* is the best show ever, but Game of Thrones is great. 2. constrain claim: It (Game of Thrones) is certainly the best fantasy show ever. 3. avoid agreement: I’ve heard it’s really popular. 4. ambivalent comment: It’s very adult. 5. decline to commit: I don’t really know.
Many of these examples are not overtly disagreeing per se, but crucially they are not agreeing. Responses such as those in (26) do not signal that the speaker accepts the validity of the at-issue content su cient to consider it as included in the common ground. For this reason, they are considered to be disagreeing. While the constructed examples about Game of Thrones are more or less easy to analyze, for the analysis of the corpus data we need a way to robustly categorize followup content as ei- ther agreeing or disagreeing. One way to operationalize this distinction is to consider the QUD. The QUDs in (27) illustrate a potential set of QUDs for the utterance, ranging from narrow to wide and including a “presupposing” QUD, which includes the utterance’s at-issue content as a presupposition.
(27) A sample of QUDs concerning Game of Thrones Utterance: Game of Thrones is the best show ever made. 1. narrowest QUD (QUDDG): Is Game of Thrones the best show ever made? 2. slightly wider QUD: Is Game of Thrones a high quality/entertaining television show?
The utterance in (27) directly answers the two QUDs listed. A related QUD, in (28), pre- supposes a positive answer to the domain goal QUD in (27) when it is answered.
(28) Related QUD: Why is Game of Thrones the best show ever made? (This QUD presup- poses that Game of Thrones is the best show ever made.)
80 The exchange in (29) uses the response to answer a QUD from line 2 in (27), namely the narrowest QUD, and this response is picking up the conversational thread su cient to represent agreement, consistent with the use of an explanation of truth to signal agreement ((25), line 2).
(29) 1. α: Game of Thrones is the best show ever made. 2. β: The writing, the cinematography, the casting, all of it’s just so perfectly done.
The response in (29) does not answer the narrowest QUD (Is Game of Thrones the best show ever made?), but it does answer another QUD, (28) (Why is Game of Thrones the best show ever made?). This is one way of picking up the conversational thread, giving evidence that presupposes an agreeing answer to the previous QUD. An example of a disagreeing response is in (30), where there is not the same implicature of agreement because of the criticism that the speaker introduces.
(30) 1. α: Game of Thrones is the best show ever made. 2. β: It is an amazing show, although the violence does get a bit gratuitous at times.
It is not at all clear that the response in line 2 of (30) represents agreement to the at-issue content. Speaker β says the show is “amazing” but then critiques the “gratuitous” violence. It may still be the case that Speaker β agrees that Game of Thrones is the best show ever made, but that is not clear from their response. The response in (30) does not answer any of the QUDs in (27); instead it raises a negative feature of the show, namely the “gratuitous” violence. Therefore it must be interpreted as disagreement, because it is insu cient to update the common ground with the at-issue content of the triggering utterance. There are also examples in which the content of the response on its own would not be con- sidered su cient to place the at-issue content into the common ground, but the intuition of speakers (such as myself and those I have discussed this with) is that the response does in fact signal agreement; to see such an example in action, consider (31)6.
(31) 1. α: Game of Thrones is an outstanding show. 2. β: Peter Dinklage is so amazing in everything he does.
In a literal sense, someone who had no knowledge of Game of Thrones or Peter Dinklage could not be expected to make the connection between these two statements and would neces- sarily conclude that β’s comment did not signal agreement. But those who are familiar with both 6Peter Dinklage was one of the primary cast members of Game of Thrones and won an Emmy for his perfor- mance on the show.
81 the show and the actor would understand that the two statements are related, and their judge- ment would be that β’s comment does signal su cient agreement to place the information in the common ground. Correctly interpreting the response as signalling agreement requires that the listener(s) have su cient pre-existing common ground with the speaker to see the relation- ship between the two statements. Speaker β signals agreement by repsonding to a broader QUD which relies on this pre-existing common ground; (32) illustrates these requirements.
(32) Required information for a correct interpretation of (31) Required CG: Peter Dinklage portrayed a major protagonist on Game of Thrones. Broader QUD: Why is Game of Thrones an outstanding show? (This QUD presup- poses that Game of Thrones is in fact an outstanding show.)
This is not to say that the common ground cannot be expanded any further; in (31), the pre- existing common ground includes the existence of the show and that it stars that actor. That it is the best show ever made is not in the pre-existing common ground; it is only entered into the common ground with β’s response in line 2. Tocontinue with the example of Game of Thrones and (31), the preexisting common ground must include the existence of the show and the actor’s role therein; the at-issue content concerns the quality of the show, and that needs to be negotiated. That the at-issue content is not in the preexisting common ground can be tested by β extending their turn to include information that contradicts the at-issue content, such as in (33).
(33) 1. α: Game of Thrones is the best show ever made. 2. β: Peter Dinklage is such an outstanding actor that he makes everything he’s in better. Which is good for Game of Thrones, because the rest of the cast is so abysmal that Peter Dinklage is the only good thing about that show.
Finally, it will be observed that there is no “middle option” between agreement and disagree- ment in coding of my data. This is because the question is essentially binary: Does the followup content signal su cient agreement, without any additional information, such that the at-issue content may be understood to be accepted into the common ground? If the answer is no, this does not necessarily mean that the information is excluded from the common ground forever; it simply means that more discussion is required rst, discussion which will expand the common ground.
82 3.1.4 Determining the question under discussion
Identifying the QUD for a given utterance is important because, in addition to supporting an understanding of whether the at-issue content enters the common ground, the QUD itself can be a target for a response. However, identifying the QUD is not as straighforward as simply selecting a question that is answered by a given utterance. By using the concepts discussed in Sec. 2.2.1.2 of Domain Goal (Roberts, 2011) and Current Question (Simons et al., 2016), it is possible to operationalize this variable. Operationalization begins with providing working de nitions for both terms in such a way that it is possible to evaluate clearly whether a given question meets that de nition or not.
(34) Operational definitions of types of Questions Under Discussion (QUDs) Domain Goal (DG): The narrowest QUD which can be answered by the utterance in question, and to which one other answer is possible; usually a yes/no question. Current Question (CG): A question to which a set of answers is possible, in- cluding the utterance in question and at least one al- ternative answer. State of the World QUD: The maximal question to which any utterance can pro- vide an answer; the set of all other QUDs.
To illustrate these de nitions in action, I will use the rst complete clause of Jane Austen’s Persuasion, edited to remove most adjuncts:
(35) Opening of Persuasion: Sir Walter Elliot, of Kellynch Hall, in Somersetshire, was a man who, for his own amusement, never took up any book but the Baronetage.
Simplifying the sentence yields a single clause which identi es the at-issue content.
(36) Abridged opening of Persuasion: Sir Walter Elliot was a man who never took up any book but the Baronetage.
Under the criteria for Domain Goals speci ed in (34), the QUDDG for (36) is in (37).
83 (37) DGx = “Was Sir Walter Elliot a man who took up any book but the Baronetage?” = {p; ¬p}, where p = “Sir Walter Elliot was a man who takes up at least one book other than the Baronetage.”
This is the narrowest, i.e. the most specifc, question to which (36) can provide an answer, satisfying the rst criterion for identifying the Domain Goal QUD. The set of possible answers includes two members, p and ¬p, and that is enough to satisfy the second criterion. Taking the answer provided in the “utterance” in (36), the answer to the QUDDG is ¬p. The Current Question is less speci c in the sense that there is not a binary set of answers, p and ¬p, but it is still a question to which the utterance in question can provide an answer. As the discussion in Sec. 2.2.1.2 highlighted, a given utterance can usually provide an answer to more than one question, and all of these questions meet the de nition of the Current Question. A selection of Current Questions for (36) is in (38).
(38) Current Questions for (36) CQ1:Whatbook(s) did Sir Walter Elliot read? CQ2:Whatkind of man was Sir Walter Elliot? CQ1:Whoonly read the Baronetage? CQ4:Whowas Sir Walter Elliot?
This set of Current Questions is not exhaustive; they are generated because the at-issue con- tent of (36) provides one potential answer, in addition to other potential answers beyond the negated at-issue content. For example, (39) o fers several answers to the fourth CQ in (38).
(39) Potential answers for CQ: Who is Sir Walter Elliot? a. Sir Walter Elliot was a man who never took up any book but the Baronetage. b. Sir Walter Elliot was the father of Elizabeth, Anne and Mary. c. Sir Walter Elliot was a cash-strapped minor member of the nobility. d. Sir Walter Elliot as a widower. The importance of operationalizing the QUD is twofold: the QUD can contribute to the analysis of whether or not the at-issue content has entered the common ground, and the QUD can itself serve as a target for response particles. The bulk of this chapter, particularly Secs. 3.3 and 3.5, will provide evidence to support these claims.
84 Figure 3.1: Flowchart for determining agreeing content
3.1.5 Summarizing the methodology
Applying an analytical methodology to natural data is a complex but necessary task to ensure a consistent analysis. The utterance generates the at-issue content and the QUD, including the set of Current Questions. The response generates the response particle and the followup content. These four components are the key ingredients for interpreting the discourse. The at-issue content is generally straightforward to determine, as it is the main thrust of the utterance (see Sec. 2.2.1.1 for further discussion), and in any event I have excluded unclear utter- ances from my study (see Sec. 3.1.2, particularly the discussion on Fragmented Discourse). Sec. 3.1.3 describes the criteria for determining whether followup content is labeled ‘agreeing’ or ‘dis- agreeing’, and 3.1.4 explains how the QUD is determined. Fig. 3.1 brings all of these components together in one procedural owchart.
85 Table 3.2: Predicted appropriate responses of The Response Target Hypothesis
Outcome ‘yeah’ + ‘no’ + agree ‘yeah’ + ‘no’ + agree disagree disagree
1 Accept AIC 2 Reject AIC 3 Accept Bel, Reject AIC 4 Accept QUD 5 Reject QUD
Table 3.3: Distribution of Corpus examples, n=400
‘yeah’ + ‘no’ + agree ‘yeah’ + ‘no’ + agree disagree disagree
1 Accept AIC 132 69* 0 0 2 Reject AIC 0 0 0 95 3 Accept Bel, Reject AIC 0 0 32 0 4 Accept QUD 0 0 35 0 5 Reject QUD 0 37 0 0
* ‘No’ plus agreeing followup content signals acceptance of the at-issue content when the triggering utterance has negation.
3.2 An overview of the corpus data
The Response Target Hypothesis made the predictions presented in Table 3.2, repeated from Table 1.2 in Ch. 1. To see how the data in the corpora pattern in comparison with the Response Target Hypothesis, let us look at the distribution of the examples from my extracted data set, re ected in Table 3.3. The columns represent the four possible response combinations, the response particle plus (dis)agreeing followup content; the rows are what is entered into the common ground as a result of the responses. The cells with checkmarks in 3.2 re ect the predictions that the Response Target Hypothesis makes; these are the cells that are predicted not to be empty.7 The two most frequent 7Rejecting belief while accepting the at-issue content is a logical possibility, but it is not predicted because of the
86 outcomes, that ‘yeah’ plus agree accepts the at-issue content and ‘no’ plus disagree rejects the at- issue content, are the predictions that most theories make regarding ‘yeah’ and ‘no’, re ecting their most familiar use. The other three highlighted outcomes are the novel predictions of the Response Target Hy- pothesis. The data provides evidence that ‘yeah’ plus disagreeing followup content can accept an interlocutor’s belief while rejecting the at-issue content and that speakers can both accept and reject the QUD based on the information already established in the common ground. The presentation of the data begins with non-paradoxical responses, Sec. 3.3, which is broken into three subsections. The rst, Sec. 3.3.1, presents instances of ‘yeah’ plus agreeing content, which are accepting the at-issue content of the triggering utterance; this section corresponds to the upper lef cell in Table 3.3. The second subsection, Sec. 3.3.2, looks at the data corresponding with the third cell of the rst row of Table 3.3, where ‘no’ accepts the at-issue content of the triggering utterance because it includes negation. The third subsection, Sec. 3.3.3, discusses ‘no’ with disagreeing content and corresponds to the second row on the far right column in Table 3.3. The next section, Sec. 3.4, presents data of accepting belief and rejecting at-issue content with the use of ‘yeah’ plus disagreeing content; this is one of the novel predictions of the Response TargetHypothesis, and there are 32 instances in Table 3.3. As the rejection of belief and acceptance of the at-issue content is unattested in the tokens used in this study, no section of such data can be included for that outcome. The nal section of data, Sec. 3.5, discusses responses which target the QUD and is divided into two subsections to present data where the QUD is itself a target of the response. The rst, Sec. 3.5.1, shows ‘yeah’ plus disagreeing content signalling acceptance of the QUD, for which there were 35 examples in the data set. The second section, Sec. 3.5.2, shows how ‘no’ plus agreeing content is used to reject the QUD, for which there were 37 examples in the data set. Table 3.4 summarizes the presentation of the data within the balance of this chapter. This table is repeated at the beginning of each of the sections which are included in it, with the relevant row highlighted. Finally, for each data point below, a breakdown will follow which clearly identi es the at-issue content, QUD, belief, response particle and followup content, with colored text corresponding to the original evidence and taking into consideration the context of the utterance, especially the speaker’s utterance before the at-issue content. The QUD is the narrowest QUD available, and where a broader QUD is needed, one is provided. The QUDs provided are not exhaustive; each data point can answer other QUDs as well. What is provided is only what is necessary for the constraints against rejecting another person’s mental state, about which a speaker has no access. Rejecting another’s belief is also not attested in the corpora tokens. Therefore, for the sake of clarity, that row is not present in the table.
87 Table 3.4: Summary of corpus data and discussion sections
Outcome Response Count Sec.
Targeting only at-issue content Accept AIC ‘yeah’ + agree 132 3.3.1 (Neg) Accept AIC ‘no’ + agree 69 3.3.2 Reject AIC ‘no’ + disagree 95 3.3.3
Targeting belief Accept bel, reject AIC ‘yeah’ + disagree 32 3.4
Targeting QUD Accept QUD ‘yeah’ + disagree 35 3.5.1 Reject QUD ‘no’ + agree 37 3.5.2 analysis. This breakdown spells out all of these features based on what is linguistically present and with no further interpretation or analysis. No polarity or common ground information is added to this breakdown, and no abbreviations or placeholder variables are used. This is to make the data maximally clear for presenting an analysis of how the responses target the components, without imposing any assumptions concerning the relationship between the at-issue content, beliefs and QUD.
3.3 Non-paradoxical responses targeting only at-issue content
Non-paradoxical responses are those in which the response particle and followup content are in agreement. Responses of ‘yeah’ will be followed by followup content such as ‘it’s true’, ‘you’re right’, ‘I agree’ or a repetition of the utterance or at-issue content; all of these responses, indepen- dently and in tandem, signal agreement. Responses of ‘no’ will be followed by followup content such as ‘that’s not right’, ‘I don’t think so’ or a repetition of the utterance or at-issue content with negation added, all of which signal disagreement. The impact of non-paradoxical responses on the common ground is predicted to correspond to the polarity of the response; positive polarity responses will update the common ground, and negative polarity responses will not. However, under certain conditions, these responses can have a di ferent impact on the common ground, in particular if the triggering utterance has negation.
88 In these cases of negative concord, the absolute polarity is [−], but then the relative polarity is [agree].
3.3.1 ‘Yeah’ responses accepting at-issue content
Table 3.5: Accepting at-issue contentwith ‘yeah’ + agree highlighted (Table 3.4 repeated)
Outcome Response # Sec. Outcome Response # Sec.
Targeting only at-issue content Targeting belief Accept AIC ‘yeah’ + agree 132 3.3.1 Accept bel not AIC ‘yeah’ + disagree 32 3.4 (Neg) Accept AIC ‘no’ + agree 69 3.3.2 Targeting QUD Reject AIC ‘no’ + disagree 95 3.3.3 Accept QUD ‘yeah’ + disagree 35 3.5.1 Reject QUD ‘no’ + agree 37 3.5.2
The examples in this section are non-paradoxical and represent the most frequent use of ‘yeah’ and agreeing followup content, which signals acceptance of the at-issue content into the common ground (Pope, 1972; Ginzburg, 1995a; Farkas and Bruce, 2010, and this current study). The corpus data set I am using includes 132 examples of this type.
3.3.1.1 Direct agreement
‘Yeah’ plus agreeing content signals agreement with the previous utterance, as in (40), from the LDC. The token is taken from a larger discourse about special interest groups in small towns. Speaker A makes an abstract example of construction interests building a swimming pool, and “it” refers to this hypothetical pool. Also of note is the fact that “scream” is likely not meant to be taken literally here; rather it likely is a hyperbolic reference to anger or frustration.
(40) 1. A: you know and one you can you can only build it in one place you know and no matter where you build it somebody else is going to scream well you didn’t build one over here 2. B: yeah that’s true unfair or something yeah yeah LDC
(40’) At-issue content: No matter where you build it somebody else is going to scream… QUD: Will a building in any location cause someone to “scream” (be un- happy)?
89 Belief: Speaker A believes a building in any location will cause someone to “scream” (be unhappy) Response particle: Yeah Followup content: Thataic’s true.
Another straightforward example is (41), in which a simple response of ‘yeah’ plus agreeing followup content is enough to signal that the at-issue content introduced in the previous utter- ance has been accepted into the common ground by both speakers. In the nal clause, ‘it’ is referring to the di ferences in ownership between dogs and cats as established previously in the turn, when Speaker A is discussing the di ferences between dogs and cats. For the purposes of this dissertation, (40) and (41) have the added advantage of most closely matching the template used in the experimental data in Ch. 4.
(41) 1. A: dogs are real high maintenance you got to take them for walks and you’ve got to pay a lot you know you need to pay attention to cats and everything but it’s not quite the same thing um and um 2. B: yeah that’s true LDC
(41’) At-issue content: It’sownershipofdogsvs.cats not the same thing QUD: Is itownershipofdogsvs.cats the same thing? Belief: Itownershipofdogsvs.cats is not the same thing. Response particle: Yeah Followup content: That’saic true.
The nal example from the corpora included here shows how ‘yeah’ plus agreeing followup content can work with negation present. Line 1 in (42) includes the proposition “I never thought a clutch was disposable,” in which a dependent clause “(that) a clutch was disposable” is the object of the matrix clause. The agreeing followup content repeats this sentiment. While this example does include negation in the triggering utterance, the response is nonetheless consistent with the analysis of ‘yeah’ and agreeing followup content; the followup content includes negation as well, signalling that the at-issue content, complete with negation, is entered into the common ground.
90 (42) 1. A: and they just said well you know clutches are disposable and i said since when, brake pads are disposable you know we know that but i never thought a clutch was disposable 2. B: yeah i wouldn’t have thought so either LDC
(42’) At-issue content: ISpeaker A never thought a clutch was disposable QUD: Are clutches disposable? Belief: Speaker A did not believe that clutches were disposable (and pre- sumably now believes that they are). Response particle: Yeah Followup content: ISpeaker B wouldn’t have thought soAIC either (but presumably now does)
3.3.1.2 Entailing agreement
The examples in this section all include ‘yeah’, and they all seem to signal agreement. However, the agreement is not as absolute as in the previous set of examples, in which the followup content clearly answers the domain goal QUD in the a rmative. The examples below signal agreement by taking up the thread of the conversation, responding to a broader QUD so that agreement to the at-issue content is entailed. In (43), line 2 uses ‘yeah’ plus agreeing followup content, although there are llers in between the response particle and the content. The use of “I mean” in line 2 is not used to de ne a term, but rather to pause the discourse before delivering a potentially controversial or di cult answer. This is a common use of hedges or llers and is not inconsistent with the Response Target Hy- pothesis or the template used to constrain the data. It is also important to note that “you” in line 1 is not impersonal; it refers speci cally to the interlocutor, Jamie Shupe.
(43) 1. UNIDENTIFIED SPEAKER: …but this was the late 1970s, so you took a girl to the queen of hearts dance, and drove a big-wheel Camaro , and cut tobacco and [you] felt nothing at all like a real boy. 2. JAMIE SHUPE: Yeah, I mean, I just - I’ve never been connected to identifying as a male. I’ve never agreed that I was a male. And yeah, I just had - I had no connection to masculinity. COCA
91 (43’) At-issue content: [Jamie Shupe] felt nothing like a real boy QUD: Did JS feel like a boy? Belief: Speaker believes that JS did not feel like a boy. Response particle: yeah Followup content: JS has never been connected to identifying as a male
Example (44)8 presents ‘yeah’ apparently responding to a question, but the question should be understood as rhetorical instead of literal, insofar as no answer is expected or required. Syntac- tically the question is embedded as a quotation in another sentence. The followup content af er ‘yeah’ also looks like questions, and syntactically they are questions. But again they are intended rhetorically as a re ection of the speaker’smental state; no response is expected or required. Line 1 provides evidence of that in saying that the speakers had to “reality check with each other,” mean- ing that the experience they are describing felt unreal; in this situation, rhetorical questions like “did it happen?” and “is this a dream?” are appropriate.
(44) 1. LIANA HEDGE: We had to reality check with each other… 2. TAMARYN YODER: Yeah. 3. LIANA HEDGE: – because you’re, like , did it happen or did it not? 4. TAMARYN YODER: Yeah, is this a dream? What’s going on? 5. LIANA HEDGE: Youfeel like, all of a sudden, somebody dropped you in the middle of hell and you can’t nd your way out. LDC
(44’) At-issue content: We had to reality check with each other because youimpers.’re like, Rhetorical Question[did it happen or did it not?] QUD: Did the experience described by Liana Hedge have the quality of being unreal? Belief: Liana Hedge believes that the experience in question had the qual- ity of being unreal. Response particle: Yeah 8This example includes two ‘yeah’s,but the rst one, in line 2, is an independent response or perhaps a backchan- nel with no followup and therefore has not been considered for the purposes of this dissertation.
92 Followup content: Rhetorical Questions[is this a dream? What’s going on?]
Given the rhetorical nature of the questions in line 4, af er ‘yeah’, these can be considered as agreeing content. They echo the rhetorical question9 in line 3, which introduces the at-issue content, ‘you [an experiencer of event] question whether it [the experience] happened,’ in that they rhetorically ask similar questions. Ms. Yoder in line 4 is not trying to ascertain if she and Ms. Hedge are at that moment experiencing a literal dream state, nor is she requesting clari cation about the current circumstances. Instead she is using rhetorical questions to provide information about the surreal nature of the event in question, which agrees with the assessment of LH. By presupposing the at-issue content, the followup content in (45) signals su cient accep- tance of the at-issue content to enter it into the common ground.
(45) Context: This exchange is decribing a situation that the Unidenti ed Woman (Speaker A) experienced at work 1. UNIDENTIFIED WOMAN (SPEAKER A): But what happened was a few weeks later, I had noticed that he was on my oor speci cally, right outside our o ce every day instead of three times a week. 2. MELISSA BLOCK: Wow. So he was there more than he had been before. 3. UNIDENTIFIED WOMAN (SPEAKER A): Yeah. And so my perception of it, even though I doubt that this is what went into it - but my perception was like, oh, this feels punitive just for reporting something (laughter). COCA
(45’) At-issue content: HeManX was there more of en than heX had been before. QUD: Was Man X in the vicinity of Speaker A more than before? Belief: Melissa Block believes that Man X was there [in the vicinity of Speaker A] more than he had been before. Response particle: Yeah Followup content: … mySpeakerA perception of itAIC was that itAIC was punitive…
9That the question is not literal is clear by the quotitive “you’re like” which introduces it; no literal answer is required.
93 Speaker A in line 1 says “…he was on my oor… every day instead of three times a week.” In line 2, the other speaker says, “…he was there more than he had been before.” Speaker A uses “yeah” to agree with the p in line 2, because it, in e fect, o fers the same information as the at-issue content that Speaker A introduced in line 1. Because the at-issue content of line 1 is mirrored in line 2, it is natural that the speaker of line 1 would signal agreement with “yeah” in line 3. More to the point, the followup signals agreement through presupposing the at-issue content by referring to it with an anaphoric pronoun ‘it.’ The small exchange in (45) is also an example of least collaborative e fort and the creation of common ground, in which Speaker A introduces a description of a phenomenon, namely the increased presence of the unnamed ‘he,’ and the explanation includes extra information about locations. Ms. Block in line 2 eliminates the information about the locations, which may rea- sonably be presupposed given the setting of the event. She distills the at-issue content from line 1 into line 2, and Speaker A agrees to enter it into the common ground. The followup content signals that the information is now in the common ground, as Speaker A refers to “it” and “this” in line 3, which refer to the information captured in line 2. Finally, consider (46), in which the at-issue content in the triggering utterance is ‘you can relate to some of what she says,’ and the followup content is ‘she is awesome.’
(46) Context: This is an interview on a news broadcast in which the two speakers are discussing Judaline Cassidy, referred to here as ‘she.’ The previous conversation had identi ed Cas- sidy’saccomplishments as an advocate for women in trades and the challenges that Cassidy and Ogles have confronted as women in trades. 1. MEGYN KELLY: So, you two have that in common. I’m sure you can relate. I – I’m sure you can relate to some of what she says. 2. MALA OGLES: Yeah. She is awesome. Way to go. 3. MEGYN KELLY: Yes. COCA
(46’) At-issue content: You[Mala Ogles] can relate to some of what she [Judaline Cassidy] says QUD: Can MO relate to some of what JC says? Belief: Megyn Kelly believes that MO can relate to some of what JC says. Response particle: Yeah Followup content: SheJC is awesome.
94 In the strictest sense, the followup content cannot be said to signal agreement to the at-issue content, for the simple reason that, in their most literal sense, they are about di ferent things; the at-issue content is about the relatability of what JC says, and the followup content is about a quality of JC herself. Wecan understand the response as agreement, however, because it responds to a broader QUD, namely ‘Does MO think that JC is awesome?’ By taking up the thread of the discussion in this way, through response to a broader QUD which includes the narrower QUD, MO signals acceptance of the at-issue content into the common ground. Natural conversations are not always clear, and interlocutors may signal agreement in more subtle or hedged ways, as in examples (47) and (49). In the rst of these examples, (47), Speaker A hedges their agreement by saying, “I could see …”
(47) Context: two speakers are discussing pets in general and rats in particular 1. B: and then she grew to be you know i had pictures of her when she would sit on my hand and then she grew to be pretty big you know like a pound or something i don’t know how much and i had her for over two years and she was very a fectionate… she would you know crawl on me and she would sit like on my neck or my shoulder while i was working and things like that yeah much more than you would think 2. A: yeah i mean you know if you get them young and everything before they go kind of nuts so yeah rats are not my favorite animals in the world but i could see getting one from birth and everything LDC
(47’) At-issue content: She (the pet rat) was very a fectionate, more than youimpersonal would think. QUD: Was Speaker B’s rat an a fectionate pet? Belief: Speaker B believes that her rat was an a fectionate pet. Response particle: Yeah Followup content: Rats are not mySpeaker A favorite animals, but ISpeaker A could see getting one [as a pet] from birth…
This hedged followup content signals agreement because it responds to a broader QUD, the answer to which may presuppose an a rmative answer to the narrowest QUD, the Domain Goal QUD, as shown in (48)
95 (48) At-issue content of (47): She (the speaker’s pet rat) was very a fectionate. QUDDG of (47): Was the speaker’s rat an a fectional pet? QUDCQ of (47): Are rats a fectionate pets?
If the answer to the QUDCQ is positive, then that at least creates the implicature that the answer to the QUDDG is also positive. Example (49) signals agreement through explanation; rather than simply agreeing with the at-issue content, Speaker A o fers information which speaks to a broader QUD.
(49) 1. B: o fered that our forefathers wouldn’t have even crossed their brains to have ex- pected you know a dishwasher or a disposal or uh uh track lighting or garage door opener you know those ki- the fancy bathrooms and stu f and yet the sturdiness of them doesn’t a- al- doesn’t always measure up 2. A: yeah i think they’re they’re trying to lower costs by cheapening in a lot of areas LDC
(49’) At-issue content: The sturdiness of [modern homes] doesn’t measure up [to the stan- dards of our forefathers] QUD: Does the sturdiness of modern homes measure up [to the standards of our forefathers]? Belief: Speaker B believes that the sturdiness of modern homes does not measure up Response particle: yeah Followup content: ISpeaker A thinks they [builders] are trying to lower costs by cheap- ening in a lot of areas
The followup content provides an answer to a broader QUD, identi ed as the Current Ques- tion in (50). The answer to why modern homes are not up to previous standards entails that modern homes are not up to previous standards. It is important to recognize that the broader QUD could not be included in the set of questions comprising the original QUD associated with the triggering utterance; but the answering to this broader QUD entails agreement with the narrower QUD, by which Speaker A is signalling their agreement.
96 (50) At-issue content of (49): The sturdiness of [modern homes] doesn’t measure up [to the standards of our forefathers] QUDDG of (49): Does the sturdiness of modern homes measure up to the stan- dards of our forefathers? QUDCQ of (49): Why don’t modern homes meet the standards of previous gen- erations? Response followup content:ISpeaker A thinks they [builders] are trying to lower costs by cheap- ening in a lot of areas
3.3.2 Agreeing with negative polarity
Table 3.6: Accepting at-issue content with ‘no’ + agree highlighted (Table 3.4 repeated)
Outcome Response # Sec. Outcome Response # Sec.
Targeting only at-issue content Targeting belief Accept AIC ‘yeah’ + agree 132 3.3.1 Accept bel not AIC ‘yeah’ + disagree 32 3.4 (Neg) Accept AIC ‘no’ + agree 69 3.3.2 Targeting QUD Reject AIC ‘no’ + disagree 95 3.3.3 Accept QUD ‘yeah’ + disagree 35 3.5.1 Reject QUD ‘no’ + agree 37 3.5.2
The examples in this section contain negation in the triggering utterance, and the ‘no’ in the response agrees with this negation; it is negative concord. The agreeing followup content also contains negation and also agrees in absolute polarity with the triggering utterance negation. To illustrate the point, the followup content in the examples in this section is almost a ver- batim repitition of the triggering utterance, an unambiguous signal that the followup content agrees with the at-issue content. Consider (51):
(51) 1. A: well i guess it wasn’t a situa- i mean i guess the mother wasn’t really asked to to lie anyway i guess it was you know 2. B: no she wasn’t asked to lie mhm she just was asked um what they were just saying you know um what how did she feel about it and she said that her daughter they were not going to put up any bail and her daughter would just have to um su fer the con- sequences
97 (51’) At-issue content: The mother wasn’t really asked to to lie QUD: Was the mother not asked to lie? Belief: Speaker A believes the mother was not asked to lie Response particle: no Followup content: she wasn’t asked to lie
The last full clause in the utterance in line 1 is “the mother wasn’t really asked to […] lie,” which contains negation in the form of the contracted “was not.” The use of negation changes the at-issue content’s absolute polarity to negative; because the absolute polarity of the response is also negative, the relative polarity is [agree](Roelofsen and Farkas, 2015). A stronger a rma- tion of the at-issue content is the nearly exact repetition of the at-issue content af er the utterance. In line 2 B says “she [the mother] wasn’t asked to lie.” This agreeing followup content is itself followed by a signal of agreement, namely “mhm,” which is used conversationally to signal agree- ment (Tolins and Fox Tree, 2014). Similarly, (52) shows ‘no’ being used to respond to an utterance which contains negation. The followup content agrees with the at-issue content contained in the initial utterance, even though it is not quite so verbatim as in (51).10
(52) Peter Sagal is interviewing the astronaut Scott Kelly about Kelly’s recent stay on the Inter- national Space Station. 1. PETER SAGAL: You had cable. You watched CNN all day. 2. SCOTT KELLY: Yeah,we had CNN on while we were working. Weweren’t, like, just watching TV all day. 3. PETER SAGAL: No, the astronauts don’t do that. The president [Donald Trump] does that. The astronauts have a job.
(52’) At-issue content: Weastronauts weren’t just watching TV all day. QUD: Were the astronauts not just watching TV all day? Belief: SK believes the astronauts (himself included) were not just watching TV all day. 10Example (52) also includes a response with ‘yeah’ and followup content in line 2; this response is discussed in Section 3.4.
98 Response particle: No Followup content: The astronauts don’t do thatwatch T V all day.
In line 3 Peter Sagal responds to the at-issue content from line 2, “We[the astronauts] weren’t just watching TV all day.” As in (51), the presence of negation licenses a ‘no’ response that in fact signals agreement with the at-issue content. The followup content agrees with the at-issue content, as “that” refers to the event of astronauts watching TV all day, which is marked with sentential negation in the triggering utterance in line 2. The result is that the at-issue content “[Astronauts] were not watching TV all day.” is placed in the common ground between Sagal and Kelly, as both have signalled their agreement with this proposition. The upshot from these examples is that ‘no’ plus agreeing content11 can signal that inter- locutors agree to the truth of a statement su cient to place the at-issue content in the common ground, provided that the original utterance contains negation. This is of course negative con- cord. Of the 69 instances of ‘no’ plus agreeing content signalling agreement with the at-issue content, every one of them contains negation in both the trigger and the followup content. Con- sider (53).
(53) 1. B: the big ten didn’t fair too well in the tournament 2. A: no they sure didn’t LDC
(53’) At-issue content: The Big Ten didn’t fare too well in the tournament. QUD: Did the Big Ten not fare too well in the tournament? Belief: Speaker B believes the Big Ten did not fare too well in the tourna- ment Response particle: No Followup content: theyBig T en sure didn’t
The followup content in A’s response can only be agreeing, as it is almost identical to the at-issue content when the elided predicate is included, which is shown in (54).
11For a discussion of the categorization of “agreeing” and “disagreeing” content, see Sec. 3.1.3
99 (54) Followup content in (53): [[they (the Big Ten teams) [did [ not [fare too well ]]]]]
These examples support a conclusion that ‘no’ + agreeing followup content is able to signal acceptance of the at-issue content into the common ground, and that such a use is triggered by the presence of sentential negation in the triggering utterance.
3.3.3 ‘No’ responses rejecting at-issue content
Table 3.7: Rejecting at-issue content with ‘no’ + disagree highlighted (Table 3.4 repeated)
Outcome Response # Sec. Outcome Response # Sec.
Targeting only at-issue content Targeting belief Accept AIC ‘yeah’ + agree 132 3.3.1 Accept bel not AIC ‘yeah’ + disagree 32 3.4 (Neg) Accept AIC ‘no’ + agree 69 3.3.2 Targeting QUD Reject AIC ‘no’ + disagree 95 3.3.3 Accept QUD ‘yeah’ + disagree 35 3.5.1 Reject QUD ‘no’ + agree 37 3.5.2
Responses with ‘no’ and disagreeing followup signal that a speaker rejects the at-issue content asserted by another speaker. This combination is quite common, accounting for 95 out of 400 examples in my extracted corpus. In the analysis of Farkas and Bruce (2010), rejecting the at-issue content results in the common ground remaining unchanged by the immediate rejection (though further update is possible). Under Kri a (2015), the common ground is not updated to include the at-issue content but it does at least record the fact of the exchange, that Speaker α said x, and Speaker β disagreed. In (55), Kurtz’s utterance contains the at-issue content “Mueller did the right thing,” which Hemingway rejects with both the response particle and the followup content in line 2.
(55) 1. KURTZ: OK. Robert Mueller did the right thing by taking this agent o f the investi- gation. 2. HEMINGWAY: No, he did not do the right thing because actually congressional in- vestigators have been asking for months what happened there. COCA
(55’) At-issue content: RM did the right thing by taking this agent o f the investigation. QUD: Did RM do the right thing by taking the agent o f the investigation? Belief: Kurtz believes that RM did the right thing by taking this agent o f the investigation.
100 Response particle: No Followup content: heRM did not do the right thing
Kurtz may still hold his original belief, but the response in line 2 has rejected the at-issue con- tent from inclusion in the common ground, and it has also not signaled any kind of acceptance into the common ground of Kurtz’s belief in the at-issue content. The exchange in (56) is from a morning chat show, and it shows an example of ‘no’ plus disagreeing followup content rejecting the at-issue content.12
(56) 1. KATHIE LEE GIFFORD: And I heard that this morning, so I said, (unintelligible) I’m going to go commando. 2. HODA KOTB: No, you did not. 3. KATHIE LEE GIFFORD: I’m commando right in the very moment. 4. HODA KOTB: No, you are not. 5. KATHIE LEE GIFFORD: I am too. And I likey. 6. HODA KOTB: No, you are not. COCA
(56’) At-issue content: IKLG am commando right in the very moment QUD: Is KLG commando at the time of utterance? Belief: KLG believes that she is “commando”. Response particle: No Followup content: youKLG are not
The exchange does not result in the update of the common ground with the at-issue content, because the speakers do not mutually agree on this information. Under the Tablemodel of Farkas and Bruce (2010), the disagreement in (56) would create a “crisis” which could only be resolved by further discourse. In these several lines of discourse, the disagreement—and “crisis”—persists with no resolution. Rejecting the truth of the at-issue content does not require that it be restated with negation, as happens in (55) and (56). The implication in (57) line 6, that liberal arts is an appropriate course 12“Going commando” is a colloquialism meaning that the speaker is not wearing underwear.
101 for those without a profession in mind, disagrees with the at-issue content that Speaker A asserts in line 5, that “he [Speaker B’s son] must have a profession in mind.”
(57) 1. A: okay he did where’d he decide to go 2. B: um to Williams College in Massachusetts 3. A: uh-huh sure i’m familiar with it 4. B: and that he was he was uh trying to decide between University of Pennsylvania and Williams and it was a very di cult choice and uh 5. A: and he well he must know is he interested in law or medicine he must have a de nite profession in mind 6. B: no but that’s one of the reasons why he chose Williams that it has solid liberal arts 7. A: oh okay COCA
(57’) At-issue content: he [Speaker B’s son] must have a de nite profession in mind QUD: Must [Speaker B’s son] have a de nite profession in mind? Belief: Speaker A believes that [Speaker B’s son] must have a de nite pro- fession in mind. Response particle: No Followup content: Thatsolid liberal arts is one of the reasons why he [Speaker B’s son] chose Williams
Speaker B rejects the at-issue content with no and disagreeing followup content, and the com- mon ground is not updated to inlcude the proposition that he must have ‘a de nite profession in mind’ af er line 6.
3.4 Accepting belief and rejecting at-issue content
One of the core predictions of the Response Target Hypothesis is that paradoxical responses can simultaneously accept a speaker’s belief while rejecting the information itself as false. This pre- diction is supported with the presence of 32 examples of this type from the extracted corpus.
102 Table 3.8: Accepting belief and rejecting at-issue content with ‘yeah’ + disagree (Table 3.4 re- peated)
Outcome Response # Sec. Outcome Response # Sec.
Targeting only at-issue content Targeting belief Accept AIC ‘yeah’ + agree 132 3.3.1 Accept bel not AIC ‘yeah’ + disagree 32 3.4 (Neg) Accept AIC ‘no’ + agree 69 3.3.2 Targeting QUD Reject AIC ‘no’ + disagree 95 3.3.3 Accept QUD ‘yeah’ + disagree 35 3.5.1 Reject QUD ‘no’ + agree 37 3.5.2
Example (58)13 includes ‘yeah,’ where ‘yeah’ plus the disagreeing followup is used to signal an aceptance of belief without accepting the at-issue content as true.
(58) Peter Sagal is interviewing the astronaut Scott Kelly about Kelly’s recent stay on the Inter- national Space Station. 1. PETER SAGAL: You had cable. You watched CNN all day. 2. SCOTT KELLY: Yeah, we had CNN on while we were working. We weren’t, like, just watching TV all day. 3. PETER SAGAL: No, the astronauts don’t do that. The president [Donald Trump] does that. The astronauts have a job. COCA
(58’) At-issue content: You [the astronauts] watched CNN all day QUD: Did the astronauts watch CNN all day? Belief: PS believes that the astronauts watched CNN all day Response particle: Yeah Followup content: We [the astronauts] had CNN on while we were working.
This example highlights that beliefs are also time sensitive, as Sagal’s response in line 3 is in- consistent with his stated belief in line 1. This requires that a belief be indexed to a speaker and a moment, as in (59).
13This example was also discussed above as (52) in this chapter.
103 (59) Indexing beliefs Indexes: Belief(speaker, p, time) From (58): Belief(Peter Sagal, p=astronauts watch CNN all day, utterance time of line 1)
The followup content disagrees because watching CNN is not part of the work of astronauts; therefore if they were working, then they were not “watching” CNN. It is common, however, for people to have music or TV on as background noise while they work. Line 1 contains Sagal’s utterance, with the at-issue content of “You [the astronauts on the International Space Station] watched CNN all day.” The response in line 2 begins with ‘yeah’ but then o fers a correction which also responds to the QUD established in line 1.
(60) QUD and answers in (58) QUD: Did the astronauts on the ISS watch CNN all day? Sagal’s answer: Yes—he asserts this (watching CNN all day) in line 1. Kelly’s answer: No—he denies this (watching CNN all day) in line 2.
The followup content in line 2, “we had CNN on while we were working” is a di ferent asser- tion than the at-issue content in line 1; Kelly further clari es by explicitly denying Sagal’sassertion, namely that the astronauts were “watching” CNN. Kelly introduces his response in line 2 with ‘yeah’ to indicate an acknowledgement of Sagal’s (mistaken) belief as expressed in line 1. Such an understanding is supported by an understanding of where the expertise resides and reasonable assumptions concerning TV habits. The expertise is on the side of Kelly, who resided at the ISS at the time in question; as such, Sagal’s assertion can only represent his belief in information he’s received, he cannot verify this information himself. Kelly accepts that Sagal believes this, but then rejects the associated at-issue content as being false. That the at-issue content does not enter into the common ground is evident from Sagal’s nal utterance, which agrees with Kelly’s assertion in line 2 that astronoauts don’t watch TV all day. A similar expression and acceptance of belief is in (61), where Speaker A bases their belief on their understanding of Texas as being hot year round. This belief is again rooted in their lack of expertise and their reliance on general knowledge and assumptions.
(61) Two speakers are discussing climate control in Texas homes 1. A: well i would imagine all you need is just air-conditioning 2. B: yeah we we have heat and some have air-conditioning units i i i have an all electric house but uh it’s f y f y LDC
104 (61’) At-issue content: All you [impersonal] need [in Texas homes] is just air-conditioning QUD: Is air-conditioning all you [impersonal] need [in Texas homes]? Belief: Speaker A believes that all that is needed [in Texas homes] is air- conditioning Response particle: Yeah Followup content: We [Texans] have heat and some have air conditioning
The followup content includes two clauses conjoined by “and,” one of which o fers a personal experience which is at odds with the at-issue content, not all and the other agrees with the at-issue content in a reduced way (some have air conditioning). The response contains ‘yeah’ and followup content that is incompatible with the at-issue con- tent in that it makes a claim that cannot be true if the at-issue content is true. If all Texans need is air conditioning, then it cannot be the case that “it’s f y f y,” which Speaker B includes in their turn. In order for the response not to be paradoxical, the two components must be target- ing di ferent types of propositions contained in the original utterance, and because the followup content contradicts the at-issue content, it must be the case that ‘yeah’ targets something other than the at-issue content for inclusion into the common ground. Given that (61) line 1 also re ects the belief of Speaker A, ‘yeah’ can felicitously accept this belief into the common ground.
3.5 Targeting the question under discussion (QUD)
The data to this point has illustrated how responses target the at-issue content and the interlocu- tor’s belief in the at-issue content, both of which can be entered into the common ground. But this does not account for other combinations found in natural language. This section will show how ‘yeah’ and ‘no’ can target the QUD without necessarily answering it. In the case of ‘yeah’, the followup content is not incompatible with the at-issue content, nor does it signal agreement to the at-issue content su cient to place it into the common ground. In the terminology of Farkas and Bruce (2010), the stack remains unpopped. In these cases, ‘yeah’ is used to signal that the QUD is appropriate and valid for discussion, as there is not currently an answer in the com- mon ground. In contrast, ‘no’ is indicating that the question is either invalid because there is already an answer in the common ground, or it is irrelevant to the discussion in the view of the responding speaker. The rst subsection will explore the use of ‘yeah’ to accept the QUD, and
105 the second section will explore the use of ‘no’ to reject the QUD, including the two reasons for rejection.
3.5.1 Accepting the QUD
Table 3.9: Accepting the QUD with ‘yeah’ + disagree highlighted(Table 3.4 repeated)
Outcome Response # Sec. Outcome Response # Sec.
Targeting only at-issue content Targeting belief Accept AIC ‘yeah’ + agree 132 3.3.1 Accept bel not AIC ‘yeah’ + disagree 32 3.4 (Neg) Accept AIC ‘no’ + agree 69 3.3.2 Targeting QUD Reject AIC ‘no’ + disagree 95 3.3.3 Accept QUD ‘yeah’ + disagree 35 3.5.1 Reject QUD ‘no’ + agree 37 3.5.2
In cases where ‘yeah’ is accepting the QUD, the followup content does not signal agreement to the at-issue content or a belief; if the triggering utterance and the followup content were ex- tracted and placed in isolation, it would not be possible to conclude that the followup content either agrees or disagrees with the at-issue content. This is not to say that the followup content is wholly unrelated, and in fact it will usually speak to a related QUD with a broader scope. The use of ‘yeah’ is to target a narrow QUD, perhaps the domain goal QUD as it is the narrowest QUD. This is an important point, because each utterance introduces several questions under discussion, but ‘yeah’ is not accepting every one of these as being valid but unanswered. The QUD which ‘yeah’ targets is narrow. Example (62) provides a clear example from a natural exchange. The example presents a ques- tion and response, but the response is notable because the speaker of line 2, Gingrich, says ‘yeah,’ and then answers the question. The fact that ‘yeah’ is not his answer to the question is clear from the followup content, in which he identi es his answer explicitly as ‘sure.’
(62) 1. BARBARO: Does he [Trump]have the mental tness, the kind of psychological suit- ability to the o ce of the presidency? 2. GINGRICH: [pause] Yeah, and my answer would be, sure. COCA
(62’) At-issue content: Trump has the mental tness or psychological suitability to be the president. QUD: Does Trump have the mental tness or psychological suitability to be the president?
106 Belief: Barbaro believes that Trump may or may not have the mental t- ness or psychological suitability to be the president Response particle: Yeah Followup content: myGingrich answer would be, sure
Gingrich explicitly separates the acceptance of the QUD with ‘yeah’ from the proposed an- swer to the question, prefaced with a tag that the followup is the answer, not the ‘yeah’. Here ‘yeah’ is accepting the QUD, as expressed in the question in line 1, as a valid, unanswered ques- tion under discussion. The question of Trump’s psychological suitability to the presidency is appropriate in the sense that it is related to the previous QUDs concerning a candidate for the presidency; a higher level QUD for the discourse is something similar to ‘Who should be presi- dent?’ or ‘Should Trump be president?’ This higher level QUD is unresolved in the sense that there is no information that would answer the question and that has been entered into the com- mon ground between those two speakers prior to this exchange. The example in (63) also shows how a response can target the QUD, even one beyond the immediate domain goal QUD. The speakers are discussing Whitney Wolfe’s14 social media com- pany, and her utterance in line 1 establishes that she is a leader in the company through her re- peated use of ‘we’ to describe the work that the company is doing.
(63) 1. WHITNEY WOLFE: … Wenow have a whole new set of moderators looking for hate symbols and hate speech on our platform. We ’re really just trying our best to build a clean, safe community. 2. GUY RAZ: Yeah. It’s been 2 1/2 years since you launched this company. 3. WHITNEY WOLFE: Correct. 4. GUY RAZ: And you have how many active users now? 5. WHITNEY WOLFE: Twenty million registrations, nearly. COCA
(63’) At-issue content: We’re [WW and the managers of Tinder] really just trying our best to build a clean, safe community QUD: Are you [WW and the managers of Tinder] really just trying your best to build a safe, clean community? 14Whitney Wolfe is the founder of Tinder and Bumble, two of the most successful dating apps. The platform she is discussing in this exerpt is Tinder.
107 Belief: WW believes that [WW and the managers of Tinder] are really just trying their best to build a safe, clean community Response particle: Yeah Followup content: It’s been 2 1/2 years since youWW launched [Tinder]. Broader QUD: How has Tinder developed since its inception?
The followup content invokes a broader QUD concerning the development of Tinder since its inception, and this QUD includes the domain goal QUD, that is the narrowest QUD. It is important to remember that utterances don’t introduce only one QUD, they each respond to a range of QUDs from the most narrow ({p, ¬p}) to the state of the world QUD (What is the state of the world?). A range of questions relating to are illustrated in (64), with the ones from (63’) in bold.
(64) a. State of the world QUD: What is the state of the world?
b. ↰ High level QUD: What is the state of online dating apps?
c. ↰ How signi cant is Tinder in the world of online dating apps?
d. ↰ How has Tinder developed since its inception?
e. ↰ What has Tinder done since its inception to grow its community of users? f. ↰ Are [WW and the managers of Tinder] trying to build a clean, safe community?
However, the followup content does not respond to the at-issue content concerning the cre- ation of a safe, clean community. This leaves the question of what ‘yeah’ is doing. One possibility is that it is accepting the at-issue content concerning a ‘clean, safe community,’ but the fact that the at-issue content is neither accepted or explicitly rejected argues against that analysis. How- ever, ‘yeah’ can accept WW’s belief in the at-issue content, and GR accepts this belief while not signalling agreement with the at-issue content. The triggering utterance in (65) is a question“What are you going to do [with AWOL sol- diers]?”, and two potential—but crucially not literal— answers. Speaker A is not suggesting that AWOL soldiers should be shot or put in jail, rather those are two non-literal suggested answers which show the real di culty of providing a real answer to the QUD. The question in line 1 is not a polar question, meaning that ‘yeah’ cannot answer it.
108 (65) 1. A: yeah well if they went awol what are you going to do shoot them put them in jail 2. B: yeah i don’t know send them over to Iraq have a vacation in Iraq for a year LDC
(65’) At-issue content: If they [soldiers] went awol [absent without leave], what are you [impersonal, societal] going to do, shoot them [the awol soldiers], put them [the awol soldiers] in jail? (Rhetorical question) QUD: What can be done with awol soldiers? Belief: Speaker A believes that ‘what are you going to do [with awol sol- diers?’ is a fair quesiton Response particle: Yeah Followup content: I don’t know
Speaker A’s initial utterance contains an if/then statement and a rhetorical question, but the speech act in the initial utterance is not a question. The e fect of the utterance may suggest that the speaker sees the question as unanswerable, similar to (66), in which the question seems to indicate the answer, in that there is nothing to be done about worsening forest res.
(66) Forest res seem to be getting worse every year, but what are you going to do?
That the questions are rhetorical derives from context and cultural knowledge, as the sug- gestion that shooting is an appropriate punishment under any circumstances is wholly unappro- priate in early 21st century North American culture. Moreover, neither speaker is in a position to decide on the punishment for awol soldiers. Speaker A knows not to expect a literal answer when they frame the question, and by o fering an equally implausible suggestion (send the soldier to Iraq for a year) they are signalling their agreement to the unanswerable nature of the question. The use of a rhetorical question signals that the speaker believes that something should be done, but they do not have an answer as to what should be done and may doubt that anything can or will be done. Speaker A suggests two potential answers to the QUD they introduced, that soldiers be ei- ther shot or jailed. Speaker B then o fers a third suggestion, sending the soldiers to Iraq, af er their initial followup content gives the truthful answer, “I don’t know.” That Speaker A o fers rhetorical questions in answer to their own QUD suggests that they do not expect a genuine an- swer from Speaker B; they do not give Speaker B time to o fer an answer. The upshot of all of these answers means that there is not, and cannot be, one agreed upon answer to the QUD.
109 As the QUD is not a yes/no question, ‘yeah’ in line 2 must be doing something other than answering the QUD. As the “at-issue content” is essentially a restatement of the QUD, there is no content which could be placed in the common ground. The only remaining function of ‘yeah’ in line 2 is to accept that the QUD is a valid QUD, one to which no answer is available in the common ground.
3.5.2 Rejecting the QUD
Table 3.10: Reject QUD with ‘no’ + agree highlighted (Table 3.4 repeated)
Outcome Response # Sec. Outcome Response # Sec.
Targeting only at-issue content Targeting belief Accept AIC ‘yeah’ + agree 132 3.3.1 Accept bel not AIC ‘yeah’ + disagree 32 3.4 (Neg) Accept AIC ‘no’ + agree 69 3.3.2 Targeting QUD Reject AIC ‘no’ + disagree 95 3.3.3 Accept QUD ‘yeah’ + disagree 35 3.5.1 Reject QUD ‘no’ + agree 37 3.5.2
The Response Target Hypothesis claims that ‘no’ plus agreeing content will signal that the QUD has been rejected because the answer is already in the common ground. This can be because the information has been discussed and there is complete agreement, as in (67), or because, in the view of at least one speaker, the question will not arrive at an answer with further discussion, as with (69). The extracted corpus provides 37 examples of the QUD being rejected for these reasons.
3.5.2.1 Answer to QUD already in common ground
Whereas a QUD is accepted as valid if there is no immediately available answer to it, a QUD is rejected as invalid if there is already an answer available. Consider (67). The at-issue content and QUD are about hurricanes, but the conversation, importantly, is about evacuees.
(67) Context: The speakers are discussing the implications of ooding in Florida and neigh- boring states. 1. HARI SREENIVASANThe other thing that concerns me is, where do they evacuate to? If they go to South Florida, that’s where the hurricane could land. 2. CRAIG CATES No, you’re absolutely right. And we have concerns over that. We were going to start running buses tomorrow to our shelter, which is in Miami at
110 Florida International University. And that’s where our shelter is. But the hurricane may hit there. So we’re looking at that COCA
(67’) At-issue content: South Florida is where the huricane could land (which could pre- sumably impact evacuees). QUD: Could a hurricane land in South Florida (which could presumably impact evacuees)? Belief: HS believes that a hurricane could land in South Florida (which could presumably impact evacuees). Response particle: No Followup content: you’reHS absolutely right
The at-issue content introduced in line 1 is that, if evacuees go to South Florida, then a spe- ci c hurricane developing in the Atlantic could land in the place where the evacuees went, South Florida. It is important to observe that this is not a claim of a causal relationship; rather it estab- lishes that the at-issue content concerns not only the hurricane landing, but rather the relation- ship of that landing event to the evacuees. The evidence is the content of the discourse before the triggering utterance and af er the followup content, all of which is concerned about the plight of ood evacuees and not the meteorology of hurricane landings. Moreover, the literal question whether a hurricane can land in South Florida is answered by shared knowledge about the world, much like “Could it rain in the Amazon?” or “Could it be hot in the Sahara?” The answer is yes to all of these questions; the only thing that makes the discussion of hurricanes salient is its relation to the discussion of evacuees. The followup content in line 2 is “you’re absolutely right,” which is agreeing. Therefore the ‘no’ response cannot be rejecting the at-issue content or the belief of Ms. Sreenivasan expressed in line 1. The answer to this QUD is clearly yes, made clear by Mr. Cates’s response in line 2. He continues his turn to indicate that he and others have concerns over the possibility that evacuees in South Florida could be hit by a hurricane. He even provides more detail about the truth of the at-issue content and answer to the QUD by stating that the shelter is in Miami, a city on the Atlantic Ocean in southern Florida. This strong evidence of shared concern over evacuees and hurricanes in southern Florida is also evidence that the information required to answer the QUD is already available in the common ground.
111 How did this information about evacuees get into the common ground of these two individ- uals? The source is community membership, those with a professional interest in weather related disasters: Sreenivasan is a journalist, Cates a disaster response manager. The community extends to people familiar with the norms of hurricane season in south Florida. This shared commu- nity membership means that both speakers can reasonably be expected to agree on the dangers of placing evacuees in south Florida during hurricane season. This function of ‘no’ rejecting the QUD also explains (68),15 repeated from Ch. 2.
(68) α: I think we need to be meeting at least once a month. β: No, I think you’re right. heard in a meeting
(68’) At-issue content: We [the group] should meet at least once a month. QUD: Is it the case that we [the group] should meet at least once a month? Belief: α believes that they [the group] should meet at least once a month Response particle: No Followup content: I [β] think you’re [α] right.
Speaker β signals explicit agreement with their followup content, “I think you’re right.” Be- cause the topic had already been discussed, it would be reasonable for Speaker β to believe the QUD in (68) had been resolved and that further discussion was unnecessary; thus, they dismiss the QUD with ‘no’. This usage of ‘no’ plus agreeing followup is particularly important, because there were 32 instances of such examples in my extracted corpus. In the Google search results in Ch. 1, Table 1.1, there were over 1.3 million examples of ‘no that’s true’ and ‘no you’re right,’ compared to just over 1 million examples of ‘no that’snot true’ and ‘no you’re not right.’ A paraphrase of this usage might be “We don’t need to discuss that, because we already both know it.”
3.5.2.2 QUD will not benefit from further discussion
Speakers may also dismiss a QUD because, at least in their view, the QUD will not bene t from further discussion because they believe that there will never be su cient agreement to establish 15This example is not from corpora and is not included in Table 3.4 or its iterations.
112 the answer in the common ground. Consider (69), in which the speakers16 disagree on most topics and may be unable to resolve an answer to the QUD.
(69) 1. CARLSON: Were they sexist for voting for Donald Trump over a feminist? 2. AREU: Well, three million people voted for Hillary Clinton. More voted for Hillary Clinton than Donald Trump. 3. CARLSON: Right. 4. AREU: So, she did get more voters. 5. CARLSON: No, but just with the sexism. I mean, I want to re-litigate the whole election. 6. AREU: Right. Right. 7. CARLSON: But just to address the question of sexism COCA
(69’) At-issue content: She [Hilary Clinton] did get more votes QUD: Did HC get more votes? Belief: Areu believes that HC got more votes. Response particle: No Followup content: But just with the sexism. Broader QUD: To what extent did sexism factor into HC losing the election even though she got more votes?
The followup content here does not respond directly to the QUD introduced in line 4, but it does speak to a broader QUD about sexism in the election. This broader QUD is related to the narrow QUD identi ed in (69’), in that an answer to a broader QUD concerning sexism—any answer—presupposes that the answer to the narrow QUD is a rmative. In (70), the relationship between the two QUDs, the narrower domain goal and the broader current question, is depicted
presupposes (70) Broader QUD (QUDCQ) −−−−−−−→ Narrow QUD (QUDDG) 16Tucker Carlson is a well-known, extremely conservative cable news host, and Cathy Arau is a Latina journalist who frequently appears as a guest on Carlson’s show.
113 QUDCQ: To what extent did sexism factor into HC losing the election even though she got more votes? QUDDG1: Did HC get more votes?
Line 1 of (69) introduces a narrow QUD that is directly linked to the broader QUD identi- ed in (69’) and referred to as QUDCQ in (70). In line 2 Areu introduces the at-issue content that “More people voted for Hillary Clinton than Donald Trump,” to which Carlson responds with agreement by saying “Right.” This is su cient to place the at-issue content in the common ground. The response particle ‘no’ in line 5 is not disagreeing with information in the common ground, it can only be rejecting the QUD because it has already been answered. The followup content in line 5, ‘But just with the sexism,’ con rms that Carlson does not view the QUD as one that will bene t from discussion.. In cases where there is no possibility of answering the QUD, responses may also reject the QUD. In (71), the at-issue content concerns personal preferences between strangers. While speak- ers can accept the at-issue content based solely on the assertion of their interlocutor, they can also reject the QUD as invalid for discussion, for the simple reason that they have nothing else to say—or discuss—once the assertion is made.
(71) 1. B: yeah see my husband’s one of these that will switch from game to game to catch a little bit of every one you know 2. A: no i like to stick to one game LDC
(71’) At-issue content: TheirSpeaker A husband switch from game to game. QUD: Does Speaker A’s husband switch from game to game? Belief: Speaker A believes that their husband switches from game to game. Response particle: No. Followup content: ISpeaker B like to stick to one game. Broader QUD: Do people switch from game to game or stick to one game?
Speaker A is in no position to discuss the at-issue content concerning Speaker B’s husband, because they have no knowledge of the husband other than what Speaker A shares. Therefore the QUD that the at-issue content responds to is not valid for discussion, because Speaker A can
114 Table 3.11: Distrubution of Corpus examples, n=400 (duplicate of Table 3.3)
‘yeah’ + ‘no’ + agree ‘yeah’ + ‘no’ + agree disagree disagree
1 Accept AIC 132 69* 0 0 2 Reject AIC 0 0 0 95 3 Accept Bel, Reject AIC 0 0 32 0 4 Accept QUD 0 0 35 0 5 Reject QUD 0 37 0 0
* ‘No’ plus agreeing followup content signals acceptance of the at-issue content when the triggering utterance has negation. never respond to the at-issue content in line 1, making “discussion” impossible. This is not to say that Speaker A cannot continue the conversation by o fering an answer to a similar QUD; Speaker A does just that in (71), answering the same QUD about their own habits rather than those of Speaker B’s husband.
3.6 Conclusions and Summary
The data presented in the previous sections covers all of the non-zero cells in Table 3.3, repeated in Table 3.11 for convenience. It demonstrates that all of the examples in the extracted corpus are predicted by the Response Target Hypothesis. The data demonstrate that ‘yeah’ can felicitously combine with disagreeing followup content, and ‘no’ can similarly combine with agreeing followup content. The explanation for this has to do with the role that response particles play in discourse. Their use is not restricted to accepting or rejecting at-issue content; they can also target beliefs and QUD. This is summarized in Table 3.12. Tokens from corpora can provide ample evidence of what speakers say, but they cannot pro- vide evidence of why speakers say those things. Also unanswered is the question concerning the update of the common ground. Demonstrating that these combinations have the hypothesized impact on the common ground requires an experimental approach. These are the questions that the next chapter intends to answer.
115 Table 3.12: Summary of corpus examples
Outcome Condition and Explanation
Accept AIC yeah + agree • ‘yeah’ and agreeing followup accept at-issue content Count: 132 • positive absolute polarity and [agree] Section: 3.3.1 • response accepts at-issue content into common ground
• negation in triggering utterance Negation: no + agree • negative absolute polarity and [agree] relative polarity Accept AIC Count: 69 • response accepts at-issue content into common ground Section: 3.3.2
• rejects at-issue content Reject AIC no +disagree • positive absolute polarity and [reject] relative polarity Count: 95 Section: 3.3.3 • ‘yeah’ accepts belief of interlocutor Accept Bel, yeah + • disagreeing followup signals rejection of the at-issue content both as a Reject AIC disagree response to the narrow QUD (Domain Goal) and the broader QUD Count: 32 (Current Question) Section: 3.4
• ‘yeah’ accepts a QUD to which both the at-issue content and followup can Accept QUD yeah + be responses disagree • requires su cient pre-existing common ground for both speakers to Count: 35 recognize the relationship between the at-issue content and followup Section: 3.5.1 content to satisfy Relevance • followup content can coexist in common ground with at-issue content
• ‘no’ dismisses QUD (Domain Goal) because the answer is already in the Reject QUD no + agree common ground Count: 37 • agreement targets either the at-issue content (which is already established) or Section: 3.5.2 the Current Goal.
116 Chapter 4
Experimental results
The examination of natural data in Chapter 3 demonstrated that ‘yeah’ and ‘no’, in tandem with agreeing and disagreeing followup, are both able to target not only at-issue content, but also beliefs and QUDs for inclusion in the common ground. However, one of the limitations of natural data is that it cannot usually provide direct comparison of response combinations, nor can it demonstrate conclusively whether or not the common ground is in fact updated. These questions are best answered by experimental means. This chapter will describe the experiment in detail, as well as the results and what they demon- strate concerning the hypothesis. An overall discussion of both experimental and natural data will be reserved for Chapter 5.
4.1 Experiment
If response particles are able to target information other than the at-issue content, then informa- tion other than the at-issue content can be included in the common ground, and the followup content of a response can target the at-issue content independent of the response particle. Based on the Response Target Hypothesis, the checkmarks in Table 4.1 indicate combinations which are predicted to be appropriate and how those combinations impact the common ground. The goal of the experiment was to determine the extent to which speakers found paradoxical responses appropriate in comparison to non paradoxical responses, and to determine whether paradoxical responses resulted in an updated common ground. Participants were asked to listen to conversations with two exchanges and to rate the acceptability of those conversations. The two exchanges consisted of an updating exchange and a common ground checking exchange, and each exchange had two lines. In the updating exchange, the triggering utterance had either a scalar or
117 Table 4.1: Experimental Predictions of Response Target Hypothesis
‘yeah’ + agree ‘no’ + agree ‘yeah’ + disagree ‘no’ + disagree
Accept AIC convergence * Reject AIC divergence Accept Bel, divergence Reject AIC Accept QUD ** Reject QUD convergence
The highlighted responses are predicted to be judged appropriate to experimental participants.
*Although ‘no’ plus agreeing content is predicted to be judged appropriate when negation is in the triggering utterance, the experiment did not test negation. See Sec. 3.3.2 for examples and discussion.
**Although ‘yeah’ plus disagreeing followup content is predicted to accept the QUD when the followup content is compatible with the at-issue content, this was not tested experimentally because no followup content was compatible with the at-issue content See Sec. 3.5.1 for examples and discussion. nonscalar predicate. The response contained either ‘yeah’ or ‘no’, and the followup content either agreed or disagreed with the at-issue content of the triggering utterance. The common ground check exchange of the discourse signalled that either the common ground was updated (a move which I term convergence) or that it was not (divergence). The purpose of the two exchange stimulus was to test which combinations of responses could impact the common ground and in what way. The Response Target Hypothesis makes predic- tions concerning what should be considered appropriate and what should not, and these predic- tions are re ected in Table 4.1. The highlighted cell in the rst row signals that ‘yeah’ plus agree- ing followup content will accept the at-issue content into the common gound when the speakers agree to the truth of the at-issue content, a condition which I term convergence. Conversely, a response of ‘yeah’ plus disagreeing followup content is predicted to be appropriate when the dis- course shows that speakers do not agree on the at-issue content, when there is divergence. Two other conditions are predicted to be appropriate: ‘no’ plus agreeing content with convergence, and ‘no’ plus disagreeing content with divergence. In terms of notation, this chapter describes the experimental design in Sec. 4.2 in the text by simply using the terminology, supplemented with abbreviations based on the rst letter of each variable primarily in tables and graphics; these two notations are listed in Table 4.2. The results
118 Table 4.2: Notation for results discussion
y ‘yeah’ a agree c converge s scalar Variables n ‘no’ d disagree d diverge n nonscalar
yeah agree converge scalar Feature labels no disagree diverge nonscalar section uses a typeface style notation to refer to the variables used in a given presentation of the stimuli, employing the notation as a kind of shorthand for ‘a discourse which included yeah and agree,’ as an example. This notation, also included in Table 4.2 will be used primarily in Sec. 4.3.
4.2 Method
4.2.1 Participants
A total of 141 participants took part in the experiment; all participants lled out a language ques- tionnaire where they were asked to identify if they were native speakers of English and if they spoke another language at home with their family. Nativeness was self-selected; no pro ciency test or other criteria were applied. The international composition of the UBC student body meant 59 participants, or 42%, were non-native speakers of English. Of the 82 remaining par- ticipants, 35 were native English speakers with no other language spoken in their home, and 47 participants were native English speakers who spoke a language other than, or in addition to, English in the home with family. As the study was primarily concerned with the judgements of English speakers, these non- native speakers were not included in the nal results. The students were recruited as part of the Linguistics Outside of the Classroom program at UBC, and since participation in an experiment is of en a course requirement, researchers cannot prohibit their participation, even if they do not otherwise meet the selection criteria. Af ern English, the largest second home language was Cantonese (14 participants), followed by Mandarin (6 participants), French (5 participants), and then Korean, Punjabi and Vietnamese with 4 each. The full list of participant languages, along with breakdowns by age and gender, are included in Table 4.3. Participants were given course credit for their participation. Age categories were in 10 year increments, except for the top age range, which was 55+; this
119 Table 4.3: Overview of English native speaker participants’ primary home languages, age and gender
Language 18-25 years 26-35 years 36-45 years TOTAL FMFMFM
English 22 7 4 1 1 35 Arabic 2 2 Cantonese 11 3 14 Farsi 2 2 French 2 2 1 5 German 1 1 Gujarati 1 1 Hebrew 1 1 Korean 3 1 4 Mandarin 5 1 6 Punjabi 3 1 4 Russian 1 1 Spanish 1 1 Vietnamese 4 4 Yoruba 1 1
TOTAL 58 17 5 0 1 1 82
Where no participant matched the criteria, spaces were lef blank for ease of reading. category and 45-55 had no participants and are not included in Table 4.3. The bottom age range begins at 18, rather than 16, to re ect the age of rst year UBC students at the time of the experi- ment.
120 4.2.2 Stimuli design
At its core, the experiment needed the stimuli to accomplish two things: (potentially) update the common ground, and then check the common ground for this (potentially) updated informa- tion. The word ‘potentially’ is present because one possible outcome is non-update of the com- mon ground, that is, the common ground could remain unchanged. As the common ground already has information from the shared community membership and previous discourse;1 addi- tional information must be agreed upon to enter the common ground2, but this does not always happen. One of the goals of this experiment was to show that information was not added to the common ground. The stimuli would need to allow for all of these possibilities to be tested by con rming the understanding of the participants. As an overview, stimuli comprised two brief exchanges of two sentences each. The rst ex- change was a two utterance conversation that took place between speakers α and β and consisted of a declarative utterance, and then a response with ‘yeah’ or ‘no’ and followup content which signalled agreement or disagreement with the at-issue content of the triggering utterance. The response (comprising two components) signalled whether information was to be added to the common ground between α and β. Af er a written description identifying a third person, ɣ, this person enters the conversation and asks a generic question relating to the previous exchange. Speaker ɣ does not have access to the common ground between α and β and their presence is strictly to test for common ground update by forcing α or β to report on the outcome of the previous discourse. The answer to ɣ’s question re ects the information in the common ground. Table 4.4 summarizes the two exchange dialogs. Presenting two small dialogs was intended to replicate the discourse process for updating and then accessing the common ground. The rst exchange is an adjacency pair which includes a presentation phase and an acceptance phase (Clark, 1996) where the acceptance may or may not update the common ground. Under the Response Target Hypothesis, a response of ‘yeah’ followed by agreeing content (‘Yeah, it was.’) should result in the common ground being up- dated with the at-issue content of the presentation phase utterance. A response of ‘no’ followed by disagreeing content should not add the presentation phase at-issue content to the common ground. The central question that the Response Target Hypothesis attempts to answer is how mixed responses, that is ‘yeah’ with disagreeing content (‘Yeah, it wasn’t’) and ‘no’ with agreeing content (‘No, it was.’) a fect the common ground, whether the common ground is updated with the at-issue content, a belief of one or the other interlocutors, or whether there is no update to 1See Sec. 2.1.1 for more details. 2See Sec. 2.3.1 for more details.
121 Table 4.4: Template of stimuli script
Purpose Speaker Utterance Variable Example
Brief written information describing the context and identifying the speakers α
Brief written information updating the context to introduce the third speaker ɣ
Check common ground don’t.) the common ground because the responses are targeting something other than at-issue content or beliefs. In the second adjacency pair, the common ground has to be checked by one of the speak- ers to see whether it was updated as a result of the rst exchange, and if so, with what. Under other analyses, such as that of Farkas and Bruce (2010), if the response in the rst exchange con- tains a ‘no’, then the common ground would not have been updated and the discourse would be in “crisis.” The logical consequence of this lack of update, however, is that the response in the second exchange would be unable to signal either convergence or divergence, because no such information would be available. However, by allowing responses to target multiple components of an utterance, the Response Target Hypothesis allows for felicitous followup answers; for ex- ample, even in cases of divergence, the common ground can be updated to include α’s belief in the at-issue content. The dialog in (1) will be helpful in providing an overview example, as it contains all of the component parts of the stimuli, while Table 4.5 summarizes the components of this sample ex- change.
122 (1) Sample of stimulus dialog with labels to show structure 1. α: The car chase scene was long. scalar predicate 2. β: Yeah, it was. / No, it was. response plus followup content 3. γ: How was the movie? checking question 4. α: We both think the car chase scene was long. –OR– convergence β: She thinks the car chase scene was long, but I don’t. divergence
Table 4.5: Summary of example dialog in (1)
Line Utterance part Variable Purpose
1 ‘…scene was long’ (non)scalar establish at-issue content 2 ‘yeah’ / ‘no’ response particle response part 1 2 ‘it was’* followup content response part 2 3 ‘How was the…?’ prompting check common question ground 4 ‘We both think…’ / ‘She convergent/ report common thinks … but I don’t.’ divergent response ground
4.2.2.1 Exchange I: Establish the common ground
The establishment of common ground is one of the central purposes of discourse, and the object of inquiry in this dissertation. This establishment relies on several features of discourse, many of which were discussed in Chapter 2. Many factors that can a fect interaction in discourse were held constant in this experiment, including intonation and sociological factors, but scalar and nonscalar predicates were used as a variable to examine responses under di ferent conditions.
Scalar versus nonscalar predicates The primary purpose of the initial, or triggering, utter- ance was to establish the at-issue content, question under discussion (QUD), speaker beliefs and other components which the responses may have targeted. Predicates in the triggering utterance were either scalar (including taste predicates) or nonscalar. The reason for this was to test whether
123 expertise or objective falsi ability in uenced the acceptability of ratings when the responses were held constant. The scalar predicates were all either taste predicates that would be indexed to an evaluator, namely the speaker, or they relied on a contextually-given standard for what counts as ‘long,’ etc. but about which individuals can still disagree. These predicates, in addition to the written contex- tual information, controlled for expertise, as each speaker would be the ‘expert’ on their opinion, and no other information was given. For example, when the speakers were discussing whether the car chase scene was ‘long,’ there was no indication of exactly how long it lasted, meaning that experimental participants could not draw their own conclusions about the length of the scene. Non-taste scalar predicates do di fer from taste predicates in that, with the former, the truth is not wholly a matter of personal opinion; no one would consider a 1 second car chase scene to be ‘long.’ But whether a 1 minute or a 5 minute or a 23 minute car chase scene is ‘long’ is, at least to some extent, a matter of personal opinion. The contexts of the stimulus items indicated that both speakers had access to the same experience but had not discussed it prior, so that the speakers have no knowledge of their interlocutor’s opinion before the conversation that is presented. A similar context was created for the nonscalar predicates, but the key di ference was that no expertise was presented, so that both interlocutors were drawing from the same limited knowl- edge as to the veracity of the claim. In other words, the non-scalar predicates did not rely on the opinions of the participants, only their (lack of) knowledge and assumptions about what may or may not have happened. All of the nonscalar predicates describe “life events,” such as getting married or buying a house, and they were all essentially binary in that either the event did or did not happen. The context is generally that the speakers are all friends with two people, for exam- ple Bill and Kathy, and they’re discussing whether Bill and Kathy (neither of whom are part of the discourse) got engaged. The text on the slide speci cally indicates that none of the discourse participants have any expertise on the question, none know for sure, but they all have the same knowledge of Bill and Kathy to suppose (or not) that the given life event did (or did not) occur.
Response particle and followup content responses Af er the initial utterance, β responded with a two part response that included a response particle (‘yeah’ or ‘no’) and appropriate agree- ing or disagreeing content (e.g. ‘it was’ or ‘it wasn’t’). The exact agreeing or disagreeing content depended on the previous utterance, so that if the rst utterance was ‘Bill and Kathy got married this weekend,’ the agreeing/disagreeing content was ‘they did’ or ‘they didn’t’, whereas if the rst utterance was ‘The car chase scene was really long,’ the agreeing/disagreeing content would be ‘it was’ or ‘it wasn’t’. While the speci c wording varied, the shape and length of that content was
124 held constant as much as possible. This was to create responses that were as minimally di ferent as possible while still allowing the responses to disambiguate what the response particles are doing. This disambiguation was the key reason why the responses have two parts; without the fol- lowup content af er the response particle, it is impossible to distinguish which types of proposi- tions —at-issue content, belief, QUD, etc.—a response may be targeting. If, for example, the judgements of the experimental participants indicate that at-issue content is in the common ground af er a response of ‘no, it was’, this tells us that ‘no’ must be doing something other than rejecting the at-issue content from the common ground. To complete the paradigm, some exchanges include only the response particle with no followpu content, or only the followup content with no response particle.
4.2.2.2 Exchange II: Check the common ground
One of the central claims concerning the common ground is that it is a repository of informa- tion that can then be accessed at a later point in the discourse. If something has successfully entered the common ground, then that information is necessarily available for the participants, and overhearers, to access at a later point. If information is available in the common ground, then a participant should be able to access that information in order to report it to a new interlocu- tor. This availability of information is essential to the idea of the common ground, and it is this availability and access that forms the theoretical basis of the second exchange of the dialogs, the checking phase. The addition of a third interlocutor who poses an open-ended question allows the original two speakers to report on the contents of the common ground. The scenarios were set up for participants so that no additional time has passed between the rst conversation and the second so that it was clear that no additional information would have been exchanged o f the record, as it were. The context described says that the only interaction between the speakers, outside of the adjacency pairs, was greetings, with no additional information being exchanged. The third interlocutor shares less common ground with the initial two interlocutors, and the purpose of her question is to expand the common ground between the three. The third discourse participant asks an open-ended question that is as neutral as possible while still pertaining to the previous exchange. As an example, if the rst exchange is about whether Bill and Kathy got married this weekend, the question is ‘What happened this week- end?’ If the exchange is about whether a movie scene was long or not, the question is ‘How was the movie?’ (with the context that the asker knows that the other two speakers went to a movie that the asker has not seen). The key point is that nothing concerning the at-issue content is
125 presupposed by the question asked by the third speaker. The response to the open-ended question signals that the original two speakers, α and β, ei- ther agreed or disagreed to the inclusion of the at-issue content into the common ground, and the response takes one of two forms: ‘We both think that …’ or ‘<α’s name> thinks that …, but I don’t.’, where … is the at-issue content from the rst exchange. All combinations of response particles, agreeing/disagreeing content, and convergence/divergence were used, so that partici- pants evaluated ‘yeah, it was’ followed by ‘Dana thinks that … but I don’t’ as well as ‘No, they didn’t’ followed by ‘We both think that …’ and all other combinations.
4.2.3 Experimental Task
The task of the experiment participants is to rate the overall acceptability of each set of exchanges. Each stimulus consists of four utterances consistent with Table 4.4; the variables are re ected in the sample of exchanges re ected in (2)–(4).
(2) Yeah agree converge nonscalar α. The car chase scene was long. nonscalar predicate β. Yeah it was. yeah agree update common ground γ. How was the movie? to check common ground α. We both think the car chase scene was long. converge common ground updated Q. Experimental participants rate the acceptability of this exchange.
(3) No disagree converge nonscalar α. The car chase scene was long. nonscalar predicate β. No it wasn’t. no disagree common ground not updated γ. How was the movie? to check common ground α. We both think the car chase scene was long. converge common ground updated Q. Experimental participants rate the acceptability of this exchange.
(4) Yeah disagree diverge nonscalar
126 α. The car chase scene was long. nonscalar predicate β. Yeah it wasn’t. yeah disagree common ground updated? γ. How was the movie? to check common ground β. She thinks the car chase scene was long, but I don’t. diverge common ground not updated Q. Experimental participants rate the acceptability of this exchange.
Participants in the experiment have the role of overhearers, in that they are not expected to generate a reponse to any question themselves, they are only evaluating the acceptibility of what they hear. The question they are asked is ‘How appropriate is the previous conversation?’ and they are provided with a 4-point Likert scale with ‘very appropriate’, ‘appropriate’, ‘inappropri- ate’, and ‘very inappropriate.’
4.2.4 Materials
Participants completed the experiment on a computer that was present in the experiment room; they were also provided with headphones and a mouse to control the progression of stimuli. The slides were presented as a Powerpoint presentation, and participants clicked to begin audio and to advance to the next slide; other components, such as advancing the visual stimuli, were automated in relation to the audio components. The audio played when the participant clicked anywhere on the screen so that the participant could control the pacing. This section will discuss the visual aspects of the stimuli and then the audio aspects.
4.2.4.1 Visual aspects of the stimuli
Figure 4.1 presents four slides from the presentation. The audio icons to the lef of the images were not displayed to participants, but they represent the embedded audio les which played the two parts of the stimuli. The slides in Fig. 4.1 are a semi-random selection of slides which show four of the scenarios which were presented to participants. The images were drawn by a research assistant, Ivana Prpic, and included to help participants track the speakers across the exchanges. Each of the two illustrations shows a clock in the upper lef corner with a time of approximately 3:00 on both illustrations; this is to signal that little if any time has passed between the rst and second exchanges. This is important to signal that no other exchange of information has taken place, that the only discussion of, for example, the movie was presented to the experiment participant and no other (relevant, i.e. about the movie) source of
127 Figure 4.1: A selection of slides from the stimuli 128 Figure 4.1: A selection of slides from the stimuli (cont.) 129 common ground exists. The speech bubbles were lef empty so that the participants only had the spoken discourse to rely on. The coloring of the gures’ clothing (and sometimes hair and oor) changed between slides to signal that the speakers had changed between slides. At the bottom right of each slide is the question number in fairly large, bold typeface to help participants answer in the correct blank on their response sheets. In the bottom lef corner, in smaller, paler typeface, is a code to the content of the slide for our reference; Fig. 4.2 highlights the location of this code.
Table 4.6: Slide reference codes
Code Meaning
b/f scalar/nonscalar predicates
1, 3 convergent reply
2, 4 divergent reply
y/n yeah/no response
a/d agreeing/disagreeing content b/f and 1-4 are identi ers to track predicates and Figure 4.2: Reference code location on slide replies throughout the experiment. Scalar=b, nonscalar=f. (The use of b and f were held over from the planning phase of the experiment, in which the stimuli were held in columns b and f on a spreadsheet.)
The description of the contexts consistently identi es the discourse participants as friends who have seen the same movie, know the same people, or have whatever shared knowledge is re- quired for the exchanges. They are placed in settings that are neutral, in that they are having lunch or meeting for co fee; these are activities that are common for friends to participate in together. They are not placed in settings that suggest a power dynamic, such as a workplace or classroom. The illustrations despict the speakers in a minimal way, as they are not meant to be lifelike representations. However, the illustrations depict all speakers as women of indeterminate age. Their ethnicity is not depicted in the illustrations,3 although the written contexts do provide 3Hair color is depicted, including black, brown, yellow and white; green blue and pink are also used as hair
130 given names; the names are drawn from students at UBC and come from a variety of cultural and ethnic backgrounds, re ecting the multicultural character of the UBC campus. The illustrations also depict neutral locations, adorned essentially with a oor and a clock, and all speakers are shown standing. By using neutral depiction intended as a participant aid, the illustrations were designed not to create implicit power dynamic between the speakers, and to depict them in an unbiased, if simplistic, way.
4.2.4.2 Audio aspects of the stimuli
All speakers who recorded the stimuli are themselves young women between 20 and 30 years of age who are all native speakers of North American English from middle class backgrounds; a total of six voices were used, with an initial of F, H, K, M, N and S. The voices sounded distinctive to me and to the speakers themselves. However, the same cohort of voices are heard on all of the recordings. For each stimulus, three speakers were used, one each for Speaker α, Speaker β and Speaker γ. Intonation was as consistent as possible across all stimuli. In addition to being young women of similar backgrounds, all of the voice volunteers were trained linguists themselves and under- stood the importance of nding an intonational contour that was at once natural to the context and consistent across the experiment. The volunteers were recorded together to create natural conversations whenever possible, and all volunteers recorded all conversations and all roles. Con- versations were then selected for use based on the clarity of the recording, the consistency of the intonation, and a balanced presentation of voices across the stimuli, so that no single voice was dominant in the presentation. The recordings were then arranged so that no voice was heard in two adjacent conversations. The exception was that, for example, Voice F could be Speaker γ in one recording and Speaker α or β in an adjacent recording; this is because Speaker γ had only one line and it was neither the beginning or the end of the recording. Of particular importance was the intonation of the response particle and the followup con- tent. To maintain consistency, recordings were chosen that displayed similar contours in Praat. Figures 4.3 and 4.4 are one speaker, and Figures 4.5 and 4.6 are another speaker, but the overall shape of all four responses is quite similar. The response particle, ‘yeah’ or ‘no’, shows the same steep rise to an early peak and a more gradual descent to the followup content, and the response particle is close to half of the length of the entire response. The peak on ‘it’ in all four responses is close to the peak of the response particle, and the followup content trails o f on both the agreeing colors. None look natural, and all are seen on the UBC campus, on women of various ethnic backgrounds.
131 Figure 4.3: “Yeah, it was.” Figure 4.4: “Yeah, it wasn’t.”
Figure 4.5: “No, it was.” Figure 4.6: “No, it wasn’t.”
and disagreeing content with a falling intonation. These samples represent each combination of responses and are indicative of the set of stimuli as a whole.
132 (5) Sample minimal pairs of stimuli demonstrating the contrasts of convergence/divergence, agreement/disagreement and yeah/no Contrasting convergence and divergence yeah agree converge yeah agree diverge a. A: The car chase scene was really long. A: The car chase scene was really long. B: Yeah it w . B: Yeah it w . C: How was the movie? C: How was the movie? A: We both think the car chase scene B: She thinks the car chase scene was was long. long, but I don’t. Contrasting agreeing and disagreeing content yeah agree converge yeah disagree converge b. A: The car chase scene was really long. A: The car chase scene was really long. B: Yeah it was. B Yeah it wasn’t. C: How was the movie? C: How was the movie? A: Weboth think the car chase scene A: Weboth think the car chase scene w long. w long. Contrasting response particles yeah agree converge no agree converge c. A: The car chase scene was really long. A: The car chase scene was really long. B: Yeah it was. B: No it was. C: How was the movie? C: How was the movie? A: Weboth think the car chase scene A: Weboth think the car chase scene w long. w long. All eight logical combinations were included in the stimuli: yeah agree converge; yeah agree diverge; yeah disagree converge; yeah disagree diverge; no agree converge; no agree diverge; no disagree converge; no disagree diverge.
4.2.5 Procedure
All participants were required to complete the experiment at a central location at the University of British Columbia. All participants were greeted by myself or one of four research assistants,
133 Figure 4.7: Sample training slide, labeled in lower right corner and then informed of their right to withdraw their participation and briefed on the task with a script read by the researcher. Stimuli were presented as a Powerpoint slide presentation over which participants had some measure of control. Participants were able to advance between slides when they were ready, and they were able to begin the audio when they were ready, but the presentation of text and graphics was otherwise automated. The were allowed to hear the audio one time, which is why they were allowed to control when the audio played. The rst ve slides were training slides (Fig 4.7), clearly indicated in the lower right corner, and the training slides were followed by written instructions presented over two slides. During the presentation of the stimuli, distractors were included af er every four slides, and participants were asked to answer a question about a given image, such as ‘What color is this ower?’ and ‘Who is this man?’ with images that required participants to choose from more than one potential candidate. Participants recorded their responses on a paper response sheet, including responses for the practice slides and distractors. They were allowed to take as much time as they required up to an hour, and a research assistant was present the entire time to answer questions. Finally, slides were presented in a semi-random selected xed order. That is, the slides were varied for response particle, followup content, and convergence/divergence, but the same order was presented to each participant. There was no pattern to the randomization, and participants
134 Table 4.7: Slide presentation summary of Fig. 4.8, pages 136–138
Subfig. Slide action Stimulus Trigger/delay } 1 Initial context Text Beginning of slide First situation drawing Illustration 2 First exchange (declarative and response) Audio On click 3 Next context Text 4 sec. delay } 4 Second situation drawing Illustration On click Second exchange (question and reply) Audio 5 Instructions for participant Text 4 sec. delay could not use the current or previous slide to predict any subsequent slide. Most participants completed the experiment in well under the alloted hour. Af er they completed the experiment they were given a demographic questionaire which gathered information about their gender, age and linguistic background. Responses were then entered into a spreadsheet with their participant ID and demographic information, as well as the date when they completed the experiment and the research assistant who worked with the participant and entered the data. Fig. 4.8 summarizes how each slide was presented to the participants. The audio icons in the illustrations of the slides were not visible to participants; they are included here to represent the audio component and when the participants heard the stimuli in relation to the visual presenta- tion. Table 4.7 appears af er all ve sub gures and summarizes the timing of the images, audio and slide progression.
135 (1) Initial context & illustration
(2) Audio on click
Figure 4.8: Illustration of slide presentation overview in Tab. 4.7
136 (c) Next context
(d) Illustration & audio on click
Figure 4.8: Illustration of slide presentation overview in Tab. 4.7 cont.
137 (e) Instructions for participant
Figure 4.8: Illustration of slide presentation overview in Tab. 4.7 cont.
4.3 Results
The discussion of results, including tables, graphics and the statistical analysis, will use the nota- tion introduced at the opening of the chapter in Table 4.2, repeated below.
Table 4.8: Notation for results discussion (repeated from Table 4.2)
y ‘yeah’ a agree c converge s scalar Variables n ‘no’ d disagree d diverge n nonscalar
yeah agree converge scalar Feature labels no disagree diverge nonscalar
Table 4.9 presents the means of the responses, arranged by conditions. The counts of each rating for each condition are also included in Table 4.9 and depicted graphically in Fig. 4.9. The
138 Table 4.9: Average and count of responses (n=324, 4 of each type per participant)
Converge Diverge Agree Disagree Agree Disagree Yeah No Yeah No Yeah No Yeah No yac nac ydc ndc yad nad ydd ndd
Mean 1.769 1.608 3.040 3.201 3.509 2.907 2.198 1.765 SD 0.937 0.864 0.901 0.967 0.831 0.933 1.010 0.989
Count of ratings { 1 165 192 19 22 14 32 94 176 appropriate 2 92 84 68 59 23 61 117 77 { 3 44 31 118 75 67 136 68 42 inappropriate 4 23 17 119 168 219 95 45 29 average ratings for converge and diverge seem to be the inverse of each other for the identi- cal response conditions.4 This patterning is an expected outcome of the experiment; when the discourse includes yeah agree and concludes with a claim of disagreement, as with diverge conditions, it is predicted that this information mismatch should be considered inappropriate. Similarly, if the discourse signals that the speakers agree, as with converge, then a high rating of acceptability would be expected if the discourse also included yeah agree. The results in Tab. 4.9 support these expectations. The mean rating of yeah agree diverge is 3.509, which is inappropriate, and the mean of yeah agree converge is 1.769, appropriate. The more signi cant questions concern the acceptability of responses that are paradoxical, such as yeah disagree and no agree. Drawing on rst impressions, it is apparent that participants found that ‘no, it was/it did.’ (no agree) was at least as appropriate at signalling convergence of opinion (average 1.608 for no agree converge) than ‘yeah, it was/ it did’ (average of 1.769 for yeah agree converge). In other words, the paradoxical response was more appropriate than the non-paradoxical response, a nding which current theories do not predict. To look at this from another angle, Table 4.9 presents the raw count of ratings, and no agree converge was judged to be appropriate 276 4Lower ratings are more appropriate.
139 Figure 4.9: Count of Ratings times, or in 85% of responses. Most accounts of response particles would suggest that ‘no’ plus agreeing followup content is felicitous only when there is negation somewhere in the triggering utterance. But there is no negation in the triggering utterance. All of the triggering utterances in the experiment were pos- itive assertions, and the results indicate that ‘no’ can still be a felicitous response in a convergent context. Similarly the Table model (Farkas and Bruce, 2010) would suggest that the use of ‘no’ would trigger a crisis and prevent the update of the common ground; in that case ‘it was’ would play no role and the nal line of (6), indicating converge, could not be considered felicitous. However a nal line encoding diverge would be equally infelicitous if the conversation were in crisis, so that both converge and diverge would result in ratings of inappropriate. But the data clearly do not signal that the discourse is in crisis, because converge results in an average rating of appropriate. How is this to be interpreted?
(6) Example of no agree converge a. α: The car chase scene was long. b. β: No, it was. c. γ: How was the movie? d. α: We both think the car chase scene was long
The upshot is that ‘no’ plus agreeing content is in fact able to signal agreement, and this result is predicted and explained by the Response Target Hypothesis. It predicts that ‘no’ can target
140 something other than the at-issue content concerning the length of the car chase scene, that ‘no’ in this case would target the QUD, dismissing it because it is already in the common ground. Convergence indicates that the at-issue content in the rst utterance has been su ciently agreed upon that it has been placed in the common ground, despite the use of the response par- ticle ‘no’. Of the two response components, only the agreeing followup content has signalled agreement, and therefore we must conclude that the followup response, and not the response par- ticle, has had the e fect of placing the information in the common ground. Where the response in (6) is ‘no’ plus agreeing followup, there were 276 ratings of very appropriate or appropriate, or 85% of responses to this condition. That no agree is appropriate seems to support the prediction of the Response Target Hy- pothesis that responses can target something other than the at-issue content, as a paradoxical response cannot simultaneously accept and reject the at-issue content, as no agree would seem to do. The Response Target Hypothesis predicts that the second target in this condition is the QUD; by hypothesis, the response particle rejects the QUD while the followup content accepts the at-issue content into the common ground. The next question that emerges concerning para- doxical responses is whether yeah disagree, as in (7), functions in a similar way.
(7) Example of yeah disagree diverge 1. α: The car chase scene was long. 2. β: Yeah, it wasn’t. ‘yeah’ plus disagreeing followup 3. γ: How was the movie? 4. β: She thinks the car chase scene was long, but I don’t. divergent response
The bar graphs in Fig.4.10 provide a visual illustration of the relation between converge and diverge on the x axis and agree and disagree on the y axis. As Fig. 4.10 shows, in almost all experimental conditions, agree and disagree seem to have the most profound in uence on acceptability, and not the use of a particular response particle. The sets of bar graphs are arranged along the four combinations of two conditions, agree/disagree and converge/diverge. The upper lef quadrant, bars 1-2, represent agree and converge, and they show a high degree of acceptability. This seems to support two predic- tions of the Response Target Hypothesis. The rst is prediction 1, which aligns with most work on response particles in claiming that a response such as ‘yeah’ can signal agreement su cient to place information in the common ground, shown in bar 1 representing yeah agree. The second prediction is an innovation of the Response Target Hypothesis and attempts to explain
141 Figure 4.10: Stacked bar graph of acceptability ratings the paradox of no agree in speech and in the corpora. The ratings of no agree converge are represented in bar 2, which shows that it was mostly rated as appropriate. The hypothe- sis explains this by claiming that the followup content agree accepts the at-issue content while the response particle no rejects the QUD. The evidence for this analysis is that no agree has high acceptability ratings under converge, signaling that the at-issue content has entered the common ground. The lower right quadrant is similar in that it re ects the high acceptability of disagree diverge, regardless of the response particle. The remaining two quadrants show that the followup content, agree/disagree, are also crucial in in uencing unacceptability rat- ings, as neither converge disagree or diverge agree are inappropriate regardless of the response particle. The overview of the results to this point has provided an interesting snapshot of the accept- ability of di ferent conditions, and one of the key takeaways is that paradoxical responses are as appropriate as non-paradoxical responses in the right circumstances. The statistical analysis will begin by looking at the factors that were most signi cant in in uencing the acceptability ratings described above; it will then look at how those factors interact, producing a more nuanced under- standing of the conditions that led to acceptability ratings. Finally the analysis will consider the probabilities of acceptability in similar conditions, and what these probabilities indicate about paradoxical responses. The rst step is determining which of the four variables—the response particle, the followup content, the converge/diverge condition, and the predicate type—has a statistically signi cant impact on the acceptability ratings. To that end, the p-values of all four variables are presented
142 Table 4.10: P -values of four variables
p adjusted p t df
yeah no 2.509E-04 1.003E-03 3.749 153.815 agree disagree 2.995E-16 1.198E-15 9.125 159.605 converge diverge 2.202E-05 8.809E-05 -4.373 159.365 scalar nonscalar 1.365E-02 5.462E-02 2.496 150.105 in Table 4.10. These p-values were obtained with Welsh two-tailed t-tests, and the Bonferroni correction was used to adjust the p-values to reduce the possibility of falsely rejecting the null hypothesis. The only criterion which has a corrected p larger than 0.05 is the predicate type, scalar nonscalar, with a corrected p of 0.05462. Because predicate type is not statistically signi cant, the analysis will focus on the other three variables: response particle, followup content and con- vergences/divergence. The independent variables interact with each other to produce the acceptability ratings, but converge/diverge have the biggest e fect. This is because one shows that the common ground has been updated while the other shows that it has not. All else being equal, something that is appropriate under converge should be unappropriate under diverge. Therefore the interac- tions will be separated rst by converge/diverge, as in Fig. 4.11. This and the other interac- tions gures were generated with R Core Team (2019) using Wickham (2016) and Long (2019) packages. Fig. 4.11 depicts converge on the lef and diverge on the right. The top of both graphs re ects the rating of 4, very unappropriate, and the bottom of both graphs re ects the rating of 1, very appropriate, with appropriate and unappropriate falling in between. The lef edge of each graph re ects yeah, and the right edge of each re ects no. The lines represent the followup content, with agree corresponding to the mean and disagree corresponding to +1SD, or the solid line. Disagree is fairly stable in both graphs, indicating that neither yeah nor no has an in uence on the interaction of agree/disagree and converge/diverge. It is unappropriate under converge and approximating appropriate under diverge, but the stability across the graph indicates that the rating is not in uenced by the response particle. This contrasts with agree, the dashed line, which is more sensitive to the presence of a response particle, especially under diverge. The sensitivity is not extreme, in that neither line is sloped to the point of spanning
143 Figure 4.11: Interactions of followup content and yeah/no in converge and diverge a step in the rating. But it is enough to indicate that, under both converge and diverge, agree is slightly more appropriate with no than with yeah. The interactions appear more signi cant in Fig. 4.12, in which the lines represent the re- sponse particle, with the mean corresponding to yeah and +1SD corresponding to no. In both converge and diverge, the lines converge on disagree, which matches with the inelastic ac- ceptability of disagree in Fig. 4.11. The tighter distribution of the lines in Fig. 4.12 indicates that the response particle has a weaker interaction than the followup content with converge and diverge. Under converge, the presense of agree or disagree is enough to push the acceptability rating an entire step with both yeah and no. This suggests that agree/disagree has a stronger in uence on the acceptability rating than yeah/no. The same pattern holds to a lesser degree under diverge, in that the presense of disagree is enough to push the response particle mean, corresponding to yeah, approximately half a step, from just below unappropriate to just above appropriate. Fig. 4.13 shows the interactions of all three variables in terms of their mutual in uence and statistical signi cance. The graph on the lef is converge, and the graph on the right is diverge, and the x axis is the response particles. The y axis does not represent acceptability ratings, rather it represents the slope of agree/disagree. The thick band between (0, 0) and (2, 0) on the x axis of both graphs indicates the range of observed data: 0 for no RP (just agree/disagree was used in these tokens), 1 for ‘yeah’ and 2 for no; all three options were included in the data for
144 Figure 4.12: Interactions of response particle and agree/disagree in converge and diverge completeness. The bands do not correspond to the values of agree/disagree; they indicate the interactions that will be statistically signi cant. The bands, both pink and blue, indicate the area of interactions with a 95% con dence in- terval, and the constricted areas of the bands indicate a narrower range of outcomes. This means that, where the band under converge constricts to the right of ‘yeah’, the range of values of agree/disagree is small. In practical terms, because there are only three values to begin with, this means that the followup content is clustered around one particular value, likely agree. The constriction is tighter under diverge to the lef of no, meaning that the range of followup con- tent is clustered more tightly around one value. What may be the most interesting nding concerns the strength of the interactions. The slope under converge is positive, meaning that the interaction of the response particle and the followup content is more signi cant than the e fects of the two variables on their own. Inter- estingly, the slope under diverge is negative, meaning that, while the e fect of the interaction is statistically signi cant, it is not as signi cant as the e fects of the response particle and the fol- lowup content. In other words, the e fect is less than the sum of its parts. Comparing Figs. 4.11 and 4.12 provides some insight into why the interactions between the response particle and followup content are di ferent with converge and diverge. The lines under diverge pattern similarly between the two gures, with a spread of approximately one step on yeah and meeting just above appropriate on no. Because they are already patterning
145 Figure 4.13: Interactions of three variables
(converge/diverge, yeah/no, agree/disagree)
n.s. = not signi cant together, the three-way interaction is not as signi cant as the two two-way interactions between diverge and the response particle and diverge and the followup content. This patterning contrasts with the lines under converge in Figs. 4.11 and 4.12, where the di ferences are considerable. In Fig. 4.11, agree/disagree shows a wider range between the mean and +/-1SD but fairly small variation in angle. In Fig. 4.12, yeah/no has a much smaller range but a much sharper angle. Because these two variables pattern so di ferently, it follows that the interaction between them is signi cant in determining the overall rating. The interactions illuminate how the system works, while statistical probabilities provide in- sight into the larger implications of the experimental results. Using R (R Core Team, 2019) and the Modern Applied Statistics with S package (MASS, Venables and Ripley 2002), an ordered probit model was used to account for the variation between participants and the four variables which were used in the stimuli: response particle, followup content, convergent or divergent checking, and scalar or nonscalar predicates. The resulting coe cients were used to generate the probabilities presented in Table 4.11 and Fig. 4.14. Fig. 4.14 highlights the importance of the followup content and converge and diverge. Within converge, probabilities pattern along the lines of the agree or disagree, and not
146 Table 4.11: Probabilities of acceptability under experimental conditions
Stimuli Probabilities
PRP Follow Condition Very appropriate Unappro- Very Unap- up appropriate priate propriate { 1 converge 0.552 0.261 0.137 0.050
2 agree diverge 0.0314 0.103 0.229 0.637 { yeah 3 converge 0.0812 0.180 0.285 0.454 4 diverge 0.299 0.292 0.247 0.163 disagr. { 5 converge 0.548 0.262 0.139 0.051
6 agree diverge 0.112 0.211 0.293 0.383
no { 7 converge 0.080 0.178 0.284 0.459 8 diverge 0.547 0.262 0.139 0.052 disagr. “Condition” refers to the experimental conditions of convergence or divergence.
Figure 4.14: Stacked bar graph of probabilities
147 yeah or no. This indicates that the followup content is a stronger predictor of acceptability than response particle. A signi cant factor a fecting acceptability judgements was convergence, converge and diverge. This is unsurprising, because exchanges that are otherwise identical should elicit opposite accept- ability based on convergence. The two sample stimuli in (8) and (9) illustrate this point; if the response signals convergence in one instance, the identical response cannot signal divergence in the other instance. The rst three lines are identical, which means that they should either be suf- cient to place the at-issue content into the common ground, or they should not be su cient. The statistical model supported this expectation. The no disagree converge example of (8) corresponds with Bar 6 in Fig. 4.14 and the seventh row of Table 4.11, and the no disagree diverge example of (9) corresponds to Bar 8 of Fig. 4.14 and row 8 (the last row) of Table 4.11.
(8) Sample of convergence (9) Sample of divergence α:. The car chase scene was long. α:. The car chase scene was long. β:. No it wasn’t. β:. No it wasn’t. γ:. How was the movie? γ:. How was the movie? α:. We both think the car chase β:. She thinks the car chase scene was long. scene was long, but I don’t.
The acceptability ratings for converge and diverge pattern together along the dimension of followup content. The groupings in Fig. 4.14 make this relationship graphically clear, in that the columns are grouped according to convergence, and the rows are grouped according to fol- lowup content. The two bars within each quadrant bear striking similarity to each other, even though one represents yeah and the other no. Indeed, within agree converge, the probabili- ties of the four ratings are almost identitical for yeah and no, as is clear from the rst two rows in Table 4.12. The second two rows show the same pattern, in that, within disagree converge, yeah and no are almost identical. The takeaway from this nding is that the followup content plays the central role in updating the common ground, moreso than the response particles. Under converge, yeah agree has a probability of 0.813 of being rated appropriate or very appropriate. While this is consistent with predictions that ‘yeah’ signals agreement to the at-issue content being entered into the common ground, it does not explain why the identical condition with no has a probability of 0.810 of being rated appropriate or very appropriate. This result, however, is consistent with the prediction of
148 Table 4.12: Probabilities of converge, from Table 4.11
Response Very accept. Accept. Unaccept. Very unaccept.
yeah agree 0.552 0.261 0.137 0.050 no agree 0.548 0.262 0.139 0.051 yeah disagree 0.0812 0.180 0.285 0.454 no disagree 0.080 0.178 0.284 0.459 the Response Target Hypothesis that the response particle is able to target something other than the at-issue content, especially in a paradoxical response. The pattern persists in the condition of diverge as well. The non-paradoxical response of yeah agree would not be expected to be appropriate, and the probability of being rated as unappropriate or very unappropriate is 0.866 (Table 4.11 row 2); compare that to the probability of no agree diverge (Table 4.11 row 6) being rated as unappropriate or very unappropriate, which is 0.743. The paradoxical response of yeah disagree is predicted to be appropriate or very appropriate in almost 6 out of 10 times (probability of 0.591, Table 4.11 row 5) under diverge; in other words, the acceptability again lines up with the followup content and not the resopnse particle. This also patterns with the non-paradoxical response of no disagree diverge, which has a probability of being appropriate or very appropriate of 0.809 (Table 4.12 row 8). Examples (10)–(13) are samples of the conversations which are predicted to be appropriate by the Response Target Hypothesis, and they correspond to highlighted columns in Table 4.13.
(10) yeah agree converge (11) no agree converge α:. The car chase scene was long. α:. The car chase scene was long. β:. Yeah it was. β:. No it was. γ:. How was the movie? γ:. How was the movie? α:. We both think the car chase β:. Weboth think the car chase scene scene was long. was long.
149 Table 4.13: Overview of participant ratings, predictions and probabilities
Convergence Divergence Yeah No Yeah No Agr. Disagr. Agr. Disagr. Agr. Disagr. Agr. Disagr. Avg 1.769 3.040 1.608 3.201 3.509 2.198 2.907 1.765 Probabilities of: v. accept. 0.552 0.081 0.548 0.080 0.031 0.299 0.112 0.547 accept. 0.261 0.180 0.262 0.178 0.103 0.292 0.211 0.262 Total 0.813 0.261 0.807 0.258 0.134 0.591 0.323 0.809 Participants rated each stimuli as a whole on a 4-point Likert scale, with 1 being very appropriate and 4 being very unappropriate. They recorded their ratings on a paper response sheet which clearly labeled the four possible ratings. Red averages in the top row are under responses that were predicted to be appropriate; lower averages signal higher acceptability.
(12) yeah disagree diverge (13) no disagree diverge α:. The car chase scene was long. α:. The car chase scene was long. β:. Yeah it wasn’t. β:. No it wasn’t. γ:. How was the movie? γ:. How was the movie? α:. She thinks the car chase scene β:. She thinks the car chase scene was long, but I don’t. was long, but I don’t.
The overall results are in Table 4.13, and the highlighted entries re ect where the Response Target Hypothesis predicts that the conversations in the stimuli would be rated as ‘appropriate’ or ‘very appropriate’. The average ratings correspond to the probabilities in acceptability, as all three of the average ratings below 2 also have a probability of above 0.8 for being rated appro- priate or very appropriate. These are all three predicted by the Response Target Hypothesis to be appropriate. The fourth predicted appropriate outcome had an average of just above 2, and a combined appropriate probability of 0.591, meaning that it is more likely to be rated as appro- priate than unappropriate. The outcomes that were predicted by the Response Target Hypothesis to be unappropriate all had average ratings of over 3, except for no agree diverge which had an average rating of 2.907. The combined probability for being rated appropriate is below 0.333 for each of these four conditions, including no agree diverge. In summary, where the Response TargetHypothesis predicted the outcomes would be appropriate, the data indicate that this prediction is borne out, and where unappropriate outcomes were predicted, this was also borne out.
150 4.4 Discussion
4.4.1 How the experimental results support the hypothesis
Overall, the experimental results seem to support the Response Target Hypothesis. That yeah agree converge and no disagree diverge should both have high rates of acceptabil- ity is not surprising, and indeed such an outcome would be predicted by most theories. The novel predictions of the Response Target Hypothesis concern the paradoxical responses, yeah disagree and no agree, and both of them are appropriate under certain conditions. That they are appropriate suggests that listeners are viewing the response particle and followup, not as contradictions, but as components that have their own purpose. The Response Target Hypothesis predicts that when the relative polarity of the followup ut- terance is [agree], resulting in the update of the common ground, then stimuli with converge in the nal utterance should be rated as appropriate. Conversely, in discourses in which the rel- ative polarity is [reject], these stimuli should be rated as appropriate when the nal utterance re ects diverge. Such a result would indicate that the followup content is the crucial compo- nent in updating the common ground to include the at-issue content. Table 4.14 shows how the polarity of the followup patterns with update of the common ground; the red elds were rated as appropriate, consistent with the predictions of the Response Target Hypothesis. Table 4.14 presents the correlation between the experimental conditions and the theoretically grounded analysis of polarity. The conditions labeled in red were predicted to be appropriate by the Response Target Hypothesis, and this prediction was supported by the experiment. The polarity marks [+] and [−] re ect the attribute noted underneath them; Roelofsen and Farkas (2015) claim that response particles get their polarity from their referent, which were all positive assertions in the experiment, which in turn establishes the polarity. To facilitate comparison be- tween the three sets of conditions in Table 4.14, let us represent all three variable with [+] and [−] (not to be confused with absolute polarity). Within the followup content, [agree] corresponds to a plus value [+] and [reject] corresponds to a minus value [−]; these represent agree and disagree within the experiment. Within the common ground check column, [+] corresponds to an updated common ground and [−] corresponds to an unchanged common ground. What the use of [+] and [−] highlights is the relationship between the response particles (yeah and no), followup content (agree and disagree) and checking the common ground for updates (converge and diverge): where the polarity of the followup content and the common ground check are the same, the discourse is likely to be judged (very) appropriate, and where they do not match the discourses are likely to be judged (very) unappropriate.
151 Table 4.14: Polarity of responses
Convergence Divergence prp followup CG check prp followup CG check
yac + + + yad + + − ‘yeah’ [agree] updated ‘yeah’ [agree] not updated nac − + + nad − + − ‘no’ [agree] updated ‘no’ [agree] not updated ydc + − + ydd + − − ‘yeah’ [reject] updated ‘yeah’ [reject] not updated ndc − − + ndd − − − ‘no’ [reject] updated ‘no’ [reject] not updated
yac =yeah agree converge; yad =yeah agree diverge; nac =no agree converge; nad =no agree diverge; ydc =yeah disagree converge; ydd =yeah disagree diverge; ndc =no disagree converge; ndd =no disagree diverge.
4.4.2 Potential variables beyond the study 4.4.2.1 Intonation
The importance of intonation for information structure in English has received extensive atten- tion in recent years, as it plays a signi cant role in English language discourse, particularly in questions (Pope, 1972; Gunlogson, 2001, 2008; Truckenbrodt, 2009; Pruitt and Roelofsen, 2011; Tubau et al., 2015), and in response particles (Goodhue et al., 2013; Goodhue and Wagner, 2015; Tubau et al., 2015; Goodhue and Wagner, 2016; Tian and Ginzburg, 2016). Many have observed that responses to sentences or questions containing negation will carry a distinctive intonational contour, variably referred to as a verum focus prominence (Roelofsen and Farkas, 2015), a reject- ing accent (Kri a, 2013), a rising-falling intonation (Liberman and Sag, 1974) or a rising-falling- rising intonation (Pope, 1972). Experimentally, intonation is entirely possible to control, and I held intonation constant by using a “neutral” intonation. By neutral, I mean an intonation that could be consistent with a range of interpretations that would be suggested by what follows. However, “neutral” does not mean identical. The set of intonational contours used in this experiment share many similar features: the high points are approximately equal and at a modest level of amplitude; the contours have similar shapes in terms of where they rise and fall; the duration is similar (see Sec. 4.2.4.2 for details).
152 Despite the variety of possible intonations and the known interaction between intonation and interpretation, holding intonation as constant as possible has nonetheless yielded robust re- sults. By setting aside the di cult question of intonation and focusing on the content of utter- ances, this dissertation is able to o fer valuable insights into the nature of response particles in discourse, insights which may then inform further work exploring intonation.
4.4.2.2 Listener status
Most work on discourse focuses on speakers and addressees and their interpretations of the dis- course. However, my experiment relies on overhearers, not interlocutors, in that it asks them to listen to conversations and assess how appropriate the response is. It is fair to ask, therefore, about whether that sets up a clear test of common ground. To what extent can overhearers access common ground information to make assessments about its contents? Based on work by Fox Tree and her colleagues (Fox Tree and Mayer, 2008; Tolins and Fox Tree, 2015), overhearers can actually recover a substantial amount of information from an over- heard dialog. They will obviously not have access to the preexisting common ground, but they can access the updates to the common ground that occur over the course of dialog. Fox Tree and Mayer (2008) nds that overhearers are better at identifying a speci c object when they have heard the object being discussed as part of a dialog than when they only hear a monologue de- scription, even if the monologue description is more detailed. This highlights the collaborative nature of discourse and the management of the common ground. This work on what overhear- ers understand about common ground management suggests that an experiment that relies on what are essentially overhearers’ responses is an empirically valid method for testing how response particles are used to manage the common ground, especially as the object being tested is a dialog. The choice to rely on overhearers was therefore motivated by reasons that support that design, as well as reasons against relying on interlocutors. A methodology that has the participants pro- viding responses in the role of interlocutors immediately runs into the challenges of controlling for content, intonation and sociolinguistic factors, including age, gender and other interspeaker dynamics. Moreover, speakers regularly vary their intonation, and this intonational variation may carry signi cant meaning, or it may be idiosyncratic to the speaker, or it may be optional in the context of that response, or it could be explained with some other reasoning. The problem is that none of these motivations would have been recoverable from a simple recorded response, making it impossible to identify what the response particle–assuming one was used–contributed to the common ground management.
153 4.5 Summary
Two of the key takeaways from the experimental results are the strong link between the followup content and the update of the common ground, and the acceptability of paradoxical responses in the appropriate convergent/divergent condition. Taken together, these two outcomes suggest that the paradoxical response of ‘yeah, it wasn’t’ (or similar followup content) will not result in the at-issue content being entered into the common ground, while the paradoxical response of ‘no, it was’ (or similar) will place the at-issue content into the common ground.
154 Chapter 5
Conclusions
This chapter has three major goals: to consider the data as a whole, including both corpus and experimental data, and what it means for the Response Target Hypothesis; to look at what other research says about paradoxical responses and similar phenomena and how the Response Target Hypothesis contributes to this understanding; and to think about the limitations of this study and the opportunities these create for future research.
5.1 Overall conclusions
The Response Target Hypothesis makes several predictions, which are repeated in (1). This sec- tion will walk through each of these predictions and look at the data from both sources which bears on those predictions.
(1) Hypothesis: Response particles, and the linguistic content following response particles as part of a response, can target (accept or reject) at least the following types of con- tent for inclusion into the common ground: at-issue content, speaker beliefs, the Question Under Discussion. Predicted targets and outcomes of responses and followup content: At-issue content 1. β accepts the at-issue content of α’s assertion; this puts the at-issue content in the common ground as something that both speakers agree is true.
155 2. β rejects the at-issue content in α’s assertion; this excludes the at-issue content from the common ground and forces a crisis (in the terms of Farkas and Bruce, 2010), the resolution of which falls to other assertions and responses. Speaker belief in at-issue content 3. β accepts α’s belief in the at-issue content without committing themselves to the same belief; this signals a mutually shared agreement concerning α’s be- lief in the at-issue content and the acceptance of that belief into the common ground, and crucially does not necessarily put the at-issue content into the com- mon ground. * Rejecting speaker belief is not considered felicitous in most contexts. Question Under Discussion (QUD) 4. β’s response accepts the QUD as a valid question to discuss, to which an answer is not currently available in the common ground or in β’s contribution. 5. β’s response rejects the QUD because the answer is already established in the common ground or the answer is unattainable at that moment.
5.1.1 Comparing the data to the predictions
The Response Target Hypothesis suggests three components introduced by the triggering ut- terance which can serve as the targets for responses: at-issue content, which can be accepted or rejected; speaker beliefs, which can be accepted; and QUD, which can be accepted or rejected. The following three sections will look at each potential target in turn, considering what the cor- pus and experimental data suggest about these targets. The purpose here is to bring the two data streams together and consider their overall implications.
5.1.1.1 Accepting and rejecting the at-issue content
One of the core functions of responses in discourse is to accept the at-issue content into the com- mon ground, or to reject it. These are the rst two predictions of the Response Target Hypoth- esis, lines 1 and 2 of (1). Both the corpus data and experimental data showed ample support that ‘yeah’ plus agreeing content accepts the at-issue content and ‘no’ plus disagreeing content rejects it.
156 In terms of accepting the at-issue content into the common ground (line 1), the corpus data presented 132 cases of this, ranging from clear examples with responses of ‘yeah you’re right’ to examples with hedged responses such as ‘yeah I can see that…’ The experimental data also sup- ported this prediction; the combination of yeah agree converge, as in (2), had an average rating of 1.769, where 1 is very appropriate and 2 is appropriate.
(2) Example of ‘yeah’ ‘agree’ ‘converge’ (yac) 1. α The car chase scene was really long. 2. β Yeah, it was. yeah agree 3. γ How was the movie? 4. α We both think the car chase scene was long. converge
Similar results support the claims of ‘no’ and disagreeing followup acting to reject the at-issue content from the common ground. The corpora provide 95 examples of ‘no’ and disagreeing content being used to reject the at-issue content. The experiment resulted in an average rating of 1.765 for no disagree diverge, as in (3).
(3) Example of no disagree diverge (ndd) 1. α The car chase scene was really long. 2. β No, it wasn’t. no disagree 3. γ How was the movie? 4. β She thinks the car chase scene was really long, but I don’t. diverge
The corpus also provides evidence of ‘no’ plus agreeing followup content placing the at-issue content in the common ground when the triggering utterance contains negation. The experi- ment did not include negation in the triggering utterances, making this an area for further inves- tigation.
5.1.1.2 Accepting belief while rejecting at-issue content
One of the central claims of the Response Target Hypothesis concerns the use of paradoxical re- sponses, where the response particle and followup content seem to contradict each other. The hypothesis proposes that a response can accept belief while rejecting the truth of the at-issue con- tent, line 3 of (1), and this claim is supported in the data. The corpus data contains 32 examples of
157 ‘yeah’ plus disagreeing followup content, and these examples seem to be accepting a belief while rejecting the at-issue content. The experimental data also support this claim of accepting belief. If speakers can accept be- lief and reject at-issue content, then stimuli with yeah disagree diverge should be rated as appropriate, and they have an average rating of 2.198, where 2 is appropriate and 1 is very appro- priate. The combined probability that yeah disagree diverge will be rated as appropriate or very appropriate is 0.591 (see Table 4.11), which supports the claim this paradoxical response is nonetheless able to keep the at-issue content out of the common ground. It is logically possible to reject a belief while accepting the at-issue content, but I have oper- ated under the assumption that this is conversationally impossible, on the grounds that a speaker cannot know what is in another speaker’s head su cient to reject the truth of that speaker’s be- lief. However, this is a testable claim, and in discussions with others I have identi ed two areas where it might be felicitous to reject a belief while accepting the at-issue content. They are pre- sented in (4) and (5). The confound is that the responses which whould be able to reject the belief and accept the at-issue content seem to get the interpretation of rejecting the QUD and accepting the at-issue content. Testing for a rejection of belief would require a way to eliminate an interpretation which rejects the QUD.
(4) Context: A parent is speaking to a small child. The parent knows that the child had a lling lunch only a few hours ago, but that it is also approaching dinner time and the child is behaving irritably. 1. CHILD: I’m hungry. 2. PARENT: ??No, you are. No=reject belief, ‘you are’=accept at-issue content 3. PARENT: No, you are. No=reject QUD, ‘you are’=accept at-issue content
(5) Context: A couple are discussing their romantic relationship. B believes the relationship is over, but does not think that A shares that view. 1. A: This relationship has run its course. 2. B: ??No, it has. No=reject belief, ‘it has’=accept at-issue content 3. B: No, it has. No=reject QUD, ‘it has’=accept at-issue content
Separating these readings is tricky. An answer of ‘no, you’re not/it hasn’t.’ does not help, because that simply signals disagreement with the at-issue content, as in the previous section. An answer of ‘yeah, you are/it has.’ similarly signals agreement.
158 That raises the question of whether there is any response that can have the desired e fect; (6) and (7) use explicit responses without any response particles to see if the desired readings can emerge.
(6) Context: A parent is speaking to a small child. The parent knows that the child had a lling lunch only a few hours ago, but that it is also approaching dinner time and the child is behaving irritably. 1. CHILD: I’m hungry. 2. PARENT: You can’t be hungry, but we’ll eat soon. reject belief, accept at-issue content
(7) Context: A couple are discussing their romantic relationship. 1. A: This relationship has run its course. 2. B: You don’t think that, but maybe it has. reject belief, accept at-issue content
The reading of rejecting belief while accepting at-issue content is at least possible with these responses, but crucially these responses do not use response particles to achieve this e fect. They state outright that they are rejecting the belief and accepting the at-issue content. Whether this same e fect can be achieved with response particles can be tested with dialogs similar to those used in the experiment, with a third person asking, ‘How is
(8) É-hó’táheva-séste Annie. 3-win-rpt-3sg Annie.
Annie won, I hear. (Murray, 2017, p. 71)
This is an empirical question that is valid for future research.
159 5.1.1.3 Accepting and rejecting the question under discussion (QUD)
The nal prediction of the Response Target Hypothesis is line 4 of 1, that the response can target the QUD itself. The QUD can be accepted as valid but unanswerable at that moment, or it can be rejected because the information is either already in the common ground or it cannot be agreed upon at that moment, and therefore the question is not valid for discussion. The corpus data provides 35 examples of acceptance and 37 of rejection of the QUD. The acceptance examples include ‘yeah’ and disagreeing content, a combination which can also sig- nal the acceptance of the belief. The di ference between the two outcomes lies in the followup content. When the response accepts a belief and rejects the at-issue content, the followup con- tent is incompatible with the at-issue content; the at-issue content and the belief cannot both be accepted into the common ground, only the belief of the speaker. This is the common trait of responses which reject the at-issue content while accepting the belief. Responses where ‘yeah’ ac- cepts the QUD have followup content which is not at odds with the at-issue content, but which nonetheless cannot signal agreement to the at-issue content on its own. An example, (9), that I heard af er the rest of the data was gathered shows this distinction quite clearly.
(9) Two people are discussing COVID-19 at an outdoor table in summer 2020. 1. α: But with my job and working with the public I’ve probably already been exposed and just haven’t gotten sick. 2. β: Yeah I wonder about whether I’ve sorta “gotten it” and been ne. (speaker used hands to create air quotes)
The original at-issue content is that Speaker α has been exposed to COVID-19 and has ex- hibited no symptoms, and Speaker β’s followup content is not incompatible with that at-issue content. At the same time, Speaker β’s followup content does not signal acceptance of the at- issue content into the common ground; it expresses curiosity about whether β has been exposed and says nothing about α’s exposure. It disagrees insofar as it does not overtly agree. Ultimately though, the common ground is able to contain both propositions, one concerning α’s exposure, the other concerning β’s. The experiment did not test for accepting a QUD, because agree/disagree were designed to be minimally contrastive; therefore they are only “it was/it wasn’t,” “they did/they didn’t” and similar. Testing the acceptance of the QUD would require a third option, something noncom- mital like “I don’t know” or “That may be.”
160 The Response Target Hypothesis predicts that, in the experiment, no agree converge should be appropriate, because ‘no’ is dismissing the QUD because the information is already established in the common ground. This prediction is supported by the data, as this combination had an average rating of 1.608 with 1 being very appropriate. The probabilities of no agree converge being rated as very appropriate or appropriate are 0.548 and 0.262; in other words, this combination is expected to be appropriate in 8 out of 10 cases. That a paradoxical response should be so appropriate indicates that speakers are able to recover meaning from the apparent paradox. The corpus data suggest that this meaning is to reject the QUD, as it included 37 examples of ‘no’ plus agreeing followup rejecting the QUD.
5.1.2 Overall conclusions from the data
Data from both the experimental and corpus sources supports the Response Target Hypothesis, with evidence that responses can target beliefs and QUDs in addition to the at-issue content; Table 5.1 presents the predictions and the data together. The table also captures the importance of using two data streams to test the hypothesis, as the experiment on its own could not provide data for accepting the QUD or using ‘no’ to accept the at-issue content when the triggering utterance contains negation. Conversely, the corpus data on its own would be unable to demonstrate the strength of the acceptability of the paradoxical responses. One surprising outcome was the level of acceptability that experimental participants assigned to the apparently paradoxical responses when compared to the non-paradoxical responses, all of which are summarized in Fig. 5.1. The gure presents the probabilities of appropriate rat- ings for apparently paradoxical responses, ydd yeah disagree diverge and nac no agree converge and their non-paradoxical counterparts, ndd as a counterpart to ydd, and yac as a counterpart to nac. The graph reveals that for both pairs (ndd and ydd, nac and yac), the proba- blility that they will be rated as appropriate or very appropriate is high. It also reveals that these probabilities are consistent across conditions; indeed, for three of the four conditions, the accept- ability is almost identical. The paradoxical response ydd has a lower probability than the other conditions of being rated as appropriate or very appropriate. I suspect that ydd would be more appropriate to speakers with a richer followup, one that perhaps explained the reason for reject- ing the at-issue content, similar to what is found in the corpora examples; intonation may also factor into the acceptability ratings. On balance, the Response Target Hypothesis nds support from the evidence, both exper- imental and from corpora. The key points from this hypothesis are that responses can target more than the at-issue content, including the QUD and another speaker’s beliefs, and that the
161 Table 5.1: Summary of Response Target Hypothesis predictions, experimental and corpus data
Prediction of Experimental Data Corpus Data Hypothesis Predictions Result Predictions Result
1. Accept at-issue yeah agree yac with combined high incidence of 132 examples of content into converge with a probability of ‘yeah’ plus ‘agree’ ‘yeah’ and ‘agree’ common ground high probability of 0.813 appropriate examples being rated rating appropriate or very appropriate
Accept at-issue not tested in experiment, negation regular incidence 69 examples of content into w not included in any tri ering of ‘no’ plus ‘agree’ ‘no’ plus ‘agree’ common ground utteranc examples when with negation in with ‘no’when trigger includes triggering trigger contains negation utterance negation
2. Reject at-issue no disagree ndd with high incidence of 95 examples of ‘no’ content from diverge with a combined ‘no’ plus ‘disagree’ plus ‘disagree’ common ground high probability of probability of examples being rated as 0.809 appropriate or very appropriate
3. Accept belief and yeah disagree ydd with regular incidence 32 examples of reject at-issue diverge with a combined of ‘yeah’ plus ‘yeah’ plus content high probability of probability of ‘disagree’ ‘disagree’ being rated as 0.591 appropriate appropriate or rating very appropriate
4. Accept the QUD not predicted in experiment, regular incidence 35 examples with as valid but followup content either explicitly of ‘yeah’ plus ‘yeah’ plus unanswered accepted or rejected the at-issue content ‘disagree’ that was ‘disagree’ that was no compatible response that did not compatible with compatible with agree the at-issue the at-issue content content
Reject the QUD no agree nac with regular incidence 37 examples of ‘no’ converge with a combined of ‘no’ plus ‘agree’ plus ‘agree’ high probability of probability of being rated as 0.810 appropriate appropriate or rating very appropriate
162 Figure 5.1: Probabilities of appropriate ratings for paradoxical and nonparadoxical response stim- uli responses can function independently of the followup content. Under alternative hypotheses in which a response particle can only targe the at-issue content, paradoxical responses would be predicted to unappropriate, meaning that the outermost columns in Fig. 5.1 should be empty. Outside of the Response Target Hypothesis, the paradoxical responses ‘nac’ and ‘ydd’ would be predicted to be empty; there should be no probability that a paradoxical response should be ap- propriate. But the probability is in fact greater than 50% for both of these responses, an outcome which the Response Target Hypothesis predicts and explains. Similarly, alternative hypotheses would predict that paradoxical response, ‘yeah’ plus ‘disagree’ and ‘no’ plus ‘agree’, would not be present in corpora, but my research identi ed 173 paradoxical responses out of 400 total to- kens. Not only does the Response TargetHypothesis predict that paradoxical responses will have a robust presence in the corpora, it explains that presence by identifying what they are targeting beyond the at-issue content. An important question that emerges is what this all means for theories of responses and com- mon ground. It is important to notice that the Response Target Hypothesisis not incompatible with the basic structure of the Table model and the Commitment Space model, both discussed in Chapter 2. In the Table model, the projected set would simply need to be explanded, perhaps as in (10).
(10) ps = {cg ∪ p, ¬p, ∅, Bel(p), QUD}
163 The Commitment Space model would only need a way to record that a QUD was accepted as unresolved and unresolvable. Perhaps the most important impact is on the nature of response particles, in that in light of the data presented here, response particles cannot be regarded as variations of each other or even one type of category with a variety of entries. Some response particles, such as ‘yeah’ and ‘no’, are compatible with paradoxical responses, and others (I believe most others) are not. It is not coincidental that these two are also able to be used as backchannels (‘no’ in the case of negation in the triggering utterance), while other response particles cannot. This suggests that there may be at least two classes of response particles, and that more research is needed to consider how else they may di fer.
5.2 Research into ‘yeah no’
The prevalence of ‘yeah no’ in natural discourse provides the clearest example of prps being used in paradoxical responses. The theoretical question is obvious: how can one response simultane- ously accept and reject the content of a previous utterance? One possibility is that it does not simultaneously accept and reject anything, that one of the response particles is doing something else entirely. ‘Yeah’ is commonly used in backchanneling, but backchanelling does not mark the beginning of a turn, making this explanation unlikely. The most plausible solution to the para- dox is that the response as a whole accepts one thing and rejects something else (Guntly, 2016). Work on ‘yeah no’ (or ‘yeah nah’) in Australian English focuses on discourse function. Bur- ridge and Florey (2002) found 29 total tokens from 25 speakers in recordings of various length, but they observe that two individual speakers, both men in their 40s, account for 10 of those to- kens, suggesting that a larger sample is needed to draw more robust conclusions. The authors focus on functional distribution, noting that it extends beyond the assent/rejection function of ‘yeah’ and ‘no’ in isolation. Moore (2007) uses multiple corpora and observes that the discourse marker ‘yeah no’ serves “multiple functions simultaneously, as is characteristic of a discourse marker.” (Moore, 2007, p. 56). She concludes that ‘yeah no’ is a discourse marker that is frequently used “to preface sum- maries and evaluations” (Moore, 2007, p. 68) and to “create cohesion” and “perform an expressive function” (Moore, 2007, p. 58-59). Linguists have explored the use of ‘yeah no’ informally as well. In a series of blog posts, Liber- man (Liberman, 2008a,b,c) speculates that in the data he presents, both ‘yeah’ and ‘no’ are felic- itous on their own. A typical example is that in (11), which does not come from a corpus and is a
164 hypothetical example.
(11) 1. A: Did you like Colombia? 2. B: Yeah no, I loved it. Liberman (2008a)
Liberman’s explanation is that ‘yeah’ can mean ‘Yeah, I liked it so much that I loved it.’ and that ‘no’ can mean ’No, I didn’t like it, I loved it.’ But the critical point is that B does not use either ‘yeah’ or ‘no’ with the followup content of “I loved it,” the speaker uses ‘yeah no’ together, plus the followup content. Why? I would contend that the response does not really communicate the same thing as the two options with the single response particle. Liberman o fers an additional insight into the distribution of response particles.
The null hypothesis, I think, would be that yeah, yes, no, oh, etc. each has its own function, and speakers emit instances of such words randomly as functionally ap- propriate, sometimes more than one in a row. This is probably false, if only be- cause the various di ferent orders of pairs are typically di ferent in frequency: “yeah yeah’’ and “no no’’ are about equally common, but “yeah no’’ is about ve times commoner than “no yeah.’’ Liberman (2008b)
These accounts focus on describing the use of ‘yeah no’ to “manage discourse” or perform “its own function.” The observations are interesting, and they suggest a pattern that is consistent with the analysis I have proposed here. Previous work observes that there is a constrained set of possible uses and interpretations for paradoxical responses, which suggests that a constraint is operating somewhere. Previous work also suggests that response particles may accept or reject information based on criteria other than the truth value of the at-issue content; this raises the question of what those criteria might be. It seems that there is something systematic about the way response particles—and followup content—are used in tandem with one another.
5.3 Opportunities for further research
The nding that response particles can operate independantly from the followup content raises several important questions that are nonetheless beyond the scope of this dissertation. The most important questions concern the workings of the system: How do response particles select their targets? How does the followup content select its target? What constraints operate on the sys- tem?
165 Figure 5.2: Kri a’s Commitment Space Development (Kri a, 2015, p. 334)
Part of understanding the overall system of responses and common ground management is understanding how response particles select their target when multiple potential targets are avail- able. Kri a (2015) might point to a potential answer, in that the components of commitment spaces are nested, as in Fig. 5.2. The gure contains three boxes arranged one inside the other.
The outermost box is the Commitment Space, the midlevel box represents S1’s assertion of ϕ, and the innermost box representes the “conventional implicature ϕ”(Kri a, 2015, p. 334). What if this nested arrangement suggests a sequence in which targets are selected by response compo- nents? If an outer box represents belief, for example, and an inner box the at-issue content, then when the paradoxical response targeting beliefs, ‘yeah’ ‘disagree’, will target the outer layer, the belief, with ‘yeah’ and the inner layer with the followup content. This is a vague suggestion built on nothing more than my own thoughts about the problem, and it certainly is not meant to im- ply an analysis by Kri a. It is simply a conjecture for a system other than randomness which may play a role in selecting the targets of response particles. One alternative is that there is a greater degree of freedom in the system in which responses may have preferred targets but can have alternative targets as well. Under such a system, ‘yeah’ would be able to target belief if the absolute polarity of the triggering utterance and the followup content are not identical, it could target QUD if there was no possibility of answering it, and it could target the at-issue content in other circumstances. The challenge for both of these systems would be make testable predictions that can disam- biguate between them. A hierarchical system would need to allow for the followup content to target the at-issue content rst, which would allow ‘yeah’ to target the next layer; a potential test would be when the “followup content” is something like “huh”, which neither agrees or dis- agrees. The degree of freedom system would need allow the response particle to test the various conditions, perhaps by removing one of them. For example, an experiment could remove the
166 QUD by presenting two potential triggering utterances, each [+], and then a response of ‘yeah’ plus ‘disagree’ (therefore with [−]) that could respond to either trigger. The key takeaway here is that the Response Target Hypothesis shows that the system of re- sponse is more complicated than previously assumed, and it creates the opportunity to explore responses in discourse in much greater detail.
5.3.1 Intonational variation
It is well known that intonation impacts how an utterance is interpreted (Gunlogson, 2001; Goodhue and Wagner, 2015). Wealso know that intonational contours are highly variable (Liber- man and Sag, 1974). Goodhue and Wagner (2016) observe that intonational contours can signal important information such as contradiction. The variability in meaning may be due to several potential factors, including syntax, semantics, context or the demographics of the speaker and addressee. And we also know that research continues into questions of intonation and its im- pact on discourse. Given all of that information, the question becomes: How do we deal with intonation in a study like this one? To take an example, (13) includes a simple adjacency pair. Fig. 5.3 depicts several di ferent intonational contours of ‘yeah it is’ that are consistent with the message of the followup content in (13). The intonational contours are very di ferent; Fig. 5.3e has three peaks, for example, while Fig. 5.3b has a signi cant dip. These contours are di ferent enough to where it may be possible to recover the meaning of the content af er ‘it is’ without it being said explicitly.
(12) 1. A: The chocolate marquis cake at Thierry is delicious. 2. B: Yeah it is (plus additional followup)
The question is how these various contours interact with the response particles and followup content to condition interpretation. The response of ‘yeah’ plus ‘agree’, which the contours in Fig. 5.3 represent, is expected to signal agreement to the at-issue content. But to take Fig. 5.3e as an example, the explanation content, ‘And burnt co fee that’s been reheated in the microwave is delicious too’, uses sarcasm to suggest that the speaker actually disagrees with the at-issue content. In terms of the Response Target Hypothesis, the question is whether intonation can out- weigh the polarity of the response particle and followup content. Let us consider (13).
(13) The discourse associated with Fig. 5.3e 1. A: The chocolate marquis cake at Thierry is delicious. 2. B: Yeah it is. And burnt co fee that’s been reheated in the microwave is delicious too.
167 (a) Yeah it is, and so is the (b) Yeah it is, it’s literally (c) Yeah it is, but it’s so rich (d) Yeah it is, …I’ve never tiramisu. the most amazing thing you it’s impossible to eat more had anything from Thierry can do with chocolate. than a bite before.
(e) Yeah it is, And burnt (f) Yeah it is, but the triple (g) Yeah it is, I’ve just eaten coffee that’s been reheated chocolate mousse is a mil- it so much lately I’m just in the microwave is delicious lion times better. tired of it. too.
Figure 5.3: Intonation of ‘yeah it is’ from (12) (followup content written below each contour)
As mentioned, the extended followup content af er ‘it is’ suggests that Speaker B actually disagrees with the at-issue content. The question is whether the intonation alone can commu- nicate this su ciently to outweigh the message of ‘yeah’ plus ‘agree’. If Speaker B just says ‘yeah it is,’ can the intonation alone communicate that this utterance is actually rejecting the at-issue content? My suspicion is no, but this is a testable prediction.
5.3.2 Responses and Information Structure
The interaction of responses and information structure in conversation was set aside for this dis- sertation, but it is not because there is no relationship between the two. The role of focus, given- ness, newness and alternative ps and QUDs is well documented (Rooth, 1992; Stalnaker, 2002; Portner, 2007; Kri a, 2008; Brown et al., 2012; Beaver et al., 2017; Ozerov, 2018), as is the inter- action of information structure and intonation (Gunlogson, 2001; Rochemont, 2013; Goodhue and Wagner, 2015). Much of this work looks at how responses encode information beyond the at-issue content of an utterance, particularly alternatives to the at-issue content, as in (14).
(14) a. α: Paul was driving. at-issue content b. β: CHLOE was driving. alternative at-issue content, based on focus intonation
168 But what if a response particle is present? The Response Target Hypothesis adds another di- mension on which to analyze these scenarios by providing a mechanism to allow the response par- ticle and the followup content to have independent targets. An interesting question is whether there is a di ference between the two responses in (15).
(15) a. β: Yeah, CHLOE was driving. focus intonation b. β: Yeah, Chloe was driving. no focus intonation
The question is, do 9a and 9b di fer in terms of what the response particle is targeting? Does focus interact with the targets of response particles? If so, then this raises questions for how response particles select targets when focus is present.
5.3.3 Other languages
Many languages use response particles to respond to questions and to utterances, and each lan- guage’s system of response particles is unique. Chapter 2 mentions the Romanian system of da, nu and ba (Roelofsen and Farkas, 2015; Farkas, 2010) and how they instantiate absolute and rel- ative polarity. In addition to the widely known oui and non, European French also has si, which is used to contradict an assertion that contains negation (Pasquereau, 2018). Similarly, German has ja, nein and doch (Kri a, 2013; Holmberg, 2012; Repp et al., 2019), where doch is also used to contradict an assertion. In English, the evidence is mounting that ‘yes’ and ‘yeah’ are not inter- changeable (Guntly, 2016; Wiltschko, 2016, and this dissertation). A positive development in the study of response particles is the expanding body of research into understudied and non-Eurpoean languages around the world, including Japanese (Philips, 1998; Hayashi, 2010; Sudo, 2013), Lao (En eld, 2010), Catalan (Batllori and Hernanz, 2013), West Flemish (Haegeman and Weir, 2015), Cheyenne (Murray, 2009), Emb ́si (Ndongo Ibara, 2011) and others. The Response Target Hypothesis predicts that other languages might also have paradoxical responses. In particular, it predicts that we might nd other languages where ‘yeah’ plus disagree- ing content can be understood as conveying acceptance of the belief and rejection of the at-issue content. Preliminary evidence from Gitksan suggests that this might be the case.
(16) A Guks jok-t Clarissa ga’a=hl Arizona. back live-PN Clarissa LOC=CN Arizona
Clarissa moved to Arizona.
169 B Ee’, nee=dii wil-t. yes NEG=FOC do-3SG.II
Yes, she didn’t.’ Consult [Laughs] “It’san agreement when you say ee’, that’san agreement. Yes,she didn’t … I’ve heard this when I was younger when the elders were talking and they were saying similar things like this ... Ee’ needii wilt. B knows that she didn’t. A believes she did. A says Guks jokt Clarissa ga’ahl Arizona but B knows that it’s not right. B is agreeing about the person, not about the whole thing.” (emphasis mine) Matthewson (2020, with Hector Hill)
The consultant’s comment at the end is illuminating, because he o fers an explanation of his intuition. He is clear that “the whole thing,” the at-issue content, is rejected, but that the speaker agrees to “the person”. The Response Target Hypothesis as I’ve presented it predicts that the belief is being accepted into the at-issue content.
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179 Appendices
180 Appendix A
Corpus response forms from Chapter 3
The following two tables, Table A.1 and A.2, include all of the responses from the corpus data presented in Chapter 3. Their purpose is to consider the extent to which form alone can account for the impact of these responses on the common ground. The column labeled Stance in both tables of responses indicates what the speaker seems to be rejecting or accepting. In Table A.1, the cases where the stance is labeled as commit q ≠ p, this indicates that the followup content cannot be understood as signalling unambiguous agreement with the previously introduced at-issue content. In Table A.2, where stance is labeled reject p, it could as easily be labeled accept ¬p. What emerges from these tables is the observation that form alone is unable to account for the status of the common ground at the end of the followup content. The crucial semantic infor- mation is missing, particularly polarity. As an example of what is missing, the data show that ‘no’ plus agreeing content can enter information into the common ground, but only if the triggering utterance contains negation; however, the presence or absence of negation in the triggering ut- terance is not re ected in the form of the response. The form of the utterance does not preserve the original at-issue content or QUD either. The absence of information beyond the response entails that an analysis based solely on response form will be unable to successfully account for all paradoxical responses.
181 Table A.1: Responses to p—‘yeah’
RP Followup content Surface form Ex. Analysis commitment yeah that’s true commit to p 40 yeah that’s true commit to p 41 cg in aic = agree + ‘Yeah’ yeah i wouldn’t have thought so either commit to p 42 yeah I’ve never been connected to identifying as a commit to q ≠ p 43 male yeah is this a dream? what’s going on? (rhetorical) commit to q ≠ p 44 yeah and so my perception of it […] was like […] this commit to q ≠ p 45 feels punitive just for reporting something yeah She is awesome commit to q ≠ p 46 Speaker A yeah Rats are not my favorite animals, but commit to q ≠ p 47 ISpeaker A could see getting one [as a pet] from birth… yeah ISpeaker A thinks they [builders] are trying to commit to q ≠ 49 lower costs by cheapening in a lot of areas } yeah we had cnn on while we were working commit to p 58 ‘Yeah’ + dis- agree, bel in yeah we have heat and some have air conditioning commit to p 61 cg, reject aic units yeah and my answer would be sure commit to p 62 ‘Yeah’ + dis- yeah it’s been 2 1/2 years since you launched this commit to q ≠ p 63 agree, accept company QUD yeah i don’t know (send them over to Iraq paradox 65 …[rhetorical suggestions])
182 Table A.2: Responses to p—‘no’
RP Followup content Surface form Ex. Analysis commitment no he did not do the right thing reject p 55 ‘No’ + dis- no you are not reject p 56 agree, aic not in cg no but that one of the reasons why he chose reject p 57 Williams, that it has a solid liberal arts program no she wasn’t asked to lie reject p 51 ‘No’ + agree, no the astronauts don’t do that reject p 52 aic in cg no they sure didn’t reject p 53 } no you’re absolutely right paradox 67 ‘No’ + agree, no but just with the sexism reject p 69 reject QUD
183 Appendix B
Corpus Data
The corpus data included 100 examples of ‘yeah’ and 100 of ‘no’ from both the Linguistic Data Consortium (LDC) Fisher and Switchboard corpora and from the Corpus of Contemporary American English (COCA), for a total of 400 examples. Of these, 22 were included in the di- cussion of Ch. 3. The remainder are presented here. Table B.1 breaks down the number of examples by corpus. It includes the predicted outcome in the lef most column, the corresponding resopnse in the rightmost column, and the totals over- all and by corpus in between. The rst number in each cell is the overall number, and the number in parentheses indicates how many of these were discussed in Ch. 3. The nal table, Table B.2, corresponds most closely to the data presented below. The table, like the data, is organized by corpus and ‘yeah’ or ‘no’, for a total of four sections. The numbers
Table B.1: Corpus Data by outcome and corpus
Parentheses indicate the number of examples which were included in Ch. 3
Outcome Total COCA LDC Response
1 Accept AIC 132 (10) 60 (5) 72 (5) ‘yeah’ + agree Accept AIC (negation) 69 (3) 31 (2) 38 (1) ‘no’ + agree 2 Reject AIC 95 (2) 61 (2) 34 (1) ‘no’ + disagree 3 Accept Bel, Reject AIC 32 (3) 16 (1) 16 (1) ‘yeah’ + disagree 4 Accept QUD 35 (3) 23 (2) 12 (1) ‘yeah’ + disagree 5 Reject QUD 37 (2) 9 (2) 28 ‘no’ + agree
184 Table B.2: Examples in appendix
Outcome Corpus and response 1 2 3 4 5 Total
COCA ‘yeah’ 55 – 15 22 – 92 LDC ‘yeah’ 67 – 15 11 – 93 COCA ‘no’ 29 59 – – 7 95 LDC ‘no’ 37 33 – – 28 98 in Table B.2 exclude examples which were discussed in Ch. 3. The organization of the data follows Table B.2, in that sections are organized by corpus and response. Each section is further organized by whether the followup content agrees or disagrees; these subsections are organized according to the predicted outcome. No further analysis or dis- cussion is o fered; the organization is simply intended to re ect the analysis o fered in the body of the dissertation. Within these smaller sections, there is no further organization. As with all other data, transcription conventions re ect what is used in the corpus. The re- sponse particle for each data point has been bolded. As some exchanges contain more than one response particle, one exchange may serve as more than one data point. The data in this section are also not “cleaned up,” in that the data is essentially in the same form as it was presented in the database. Spaces may have been omitted between words, ‘n’t’ is of en separated in contrac- tions, capitalization and punctuation is of en missing, time stamps may be present, and other anomalies may be found. I have, however, arranged the text on lines re ecting the turns between speakers, and where a speaker label was provided, I have included it.
B.1 COCA ‘yeah’ (92 examples)
This section includes 55 examples with agreeing followup content and 37 with disagreeing con- tent. Of these 37, f een were coded as accepting belief while rejecting at-issue content, and 22 were coded as accepting the QUD.
B.1.1 COCA ‘yeah’ + agree (55 examples)
185 (1) 1. JILL MARTIN- (08#41:36): You actually – 2. SAVANNAH GUTHRIE-(#41:36): Or the kids. 3. JILL MARTIN- (08#41:37): No. But you wear that purple tinsel in your hair. 4. SAVANNAH GUTHRIE-(#41:39): Yeah That pink glitter in your hair. 5. JILL MARTIN- (08#41:40): There you go. Okay. So over twenty collections again on today.com. The retail, forty- ve to f y. The deal, twenty. Up to sixty percent o f.
(2) 1. SMITH: How much does it have to do with power? How much of it is adventure and how much of it is I’m the big politician, the head of whatever corporation, blah, blah, blah’? 2. Dr-FISHER: Well, they get a lot of opportunities. 3. SMITH: What’s that? 4. Ms-BARASH: That’s right. 5. Dr-FISHER: They get enormous number of opportunities, powerful men. 6. Ms-BARASH: And we live in a patriarchal culture. 7. Mr-TITUS: And power is sexy. 8. Dr-FISHER: Yeah, power – women love-yeah. 9. Ms-BARASH: Yeah. 10. Mr-TITUS: And women are – women are ready to, you know, make some sacri ces... 11. Ms-BARASH: Yeah. 12. Dr-FISHER: So do mice and sheep. 13. Mr-TITUS:... to sleep with some important person. 14. Ms-BARASH: And also there’s... 15. SMITH: What’d she say? 16. Dr-FISHER: So do mice and sheep. I mean, they – females of – both female species are attracted to the male with the best piece of real estate,
186 (3) 1. Mr-ORWOLL: Get hundreds of dollars of discounts. Go to londontown.com, you can buy that. 2. HOLT: Seventy- ve dollars. Then a great air fare and hotel deal. 3. Mr-ORWOLL: For – yes. That’s o fered by Liberty Travel, libertytravel.com. You’re staying at the Sheraton Belgrade, ve-star hotel, your air fare from the East Coast and three nights at the hotel. 4. HOLT: And – and another deal, air fare, six nights in a hotel at 429 per person. 5. Mr-ORWOLL: Yeah, from o fpeaktraveler.com. And that’s a budget hotel deal, but it’s six nights, if you want to stay. 6. HOLT: But – but if you want to save a few bucks and see London. Let me quickly ask you about a warm-weather destination, because I’m afraid that’s what’s on my mind af er coming in o f the plaza. 7. Mr-ORWOLL: Sure. OK. 8. HOLT: Puerto Rico and a place – Paradores. These are guest houses. Mr-ORWOLL: That’s right.
(4) 1. LAUER: That’s right. 2. CURRY: And I got to tell you, I loved you guys. You guys are so much fun. 3. Ms-BETTY CHAVES:Hi. Ann. 4. LAUER: It was fun having you here. Thank you so much. 5. Ms-CHAVES:TODAY has just been wonderful. 6. Mr-CHRISTOPHER HAS: Yeah. 7. Ms-CHAVES:We thank everybody at NBC, we’ve had marvelous experience. 8. Mr-HASLIP: Yeah. We had a great time. 9. CURRY: I hope you’ve had fun. 10. LAUER: We had a great time getting to know you. And good luck in your lives to- gether. 11. CURRY: I know, and you will always be in my mind forever doing that one dance that you were doing. It was so much fun.
187 12. LAUER: When we come back, we’re going to play the Nearly-Wed Game right here. But rst, these messages. Thanks very much.
(5) 1. CHRIS WRAGGE: Education? 2. JENNIFER HARTSTEIN: Education, why not? Go gure out ways to learn more. Theres always opportunities. Maybe you cant go back to school full-time, but you can take evening classes. They now have weekend classes. Theres online learning courses that you can take. So there really are new ways to expand the educational horizon. And you can take that chance, too. 3. CHRIS WRAGGE: All right. And career and nance. Well put those in the same category. 4. JENNIFER HARTSTEIN: Yeah, career and nance are tough. And you really have to gure out what it is you want to do. We of en think that in our twenties we have the opportunity to switch and move our careers a lot. The fact is there are people now, especially in the economic changes that are changing careers in their f ies and sixties. If theres something you love to do, gure out how to make sure your nances are in shape, so that you can switch gears and do something else
(6) 1. while he may have been President for everyone, he wasn’t doing enough for the African- American community. So can you square the two and looking back, do you think Pres- ident Obama made some missteps with regards to the African-American community now seeing what President Trump’s being accused of doing? 2. TA NEHISI COATES#Yeah. black people in this country make up I believe thirteen, fourteen percent of the population. Trump – that’s (INDISTINCT) – 3. BIANNA GOLODRYGA#Thirteen percent. 4. TA NEHISI COATES#Yeah. Trump’s base is a lot bigger, you know, so the number of people, the sheer number of voters
(7) 1. DAVE DAVIES#You had a memorable episode recently where you went to Vietnam. And you - I ca n’t remember whether you said this in an episode or whether I read it somewhere else - you said the world tilted for you in a Vietnamese rice farmer’s home.
188 2. ANTHONY BOURDAIN#Yeah. the rst time I went to Vietnam, I just - I remem- ber comingaway from it thinking, I just - I have to have more of this. This is what I want to do with the rest of my life... 3. DAVE DAVIES#More of Vietnam or more of that kind of
(8) 1. …but I did hang out a lot with some teenagers who were sort of there long-term for various chronic health problems, mostly playing video games, to be honest, (laughter). I was 22 at the time. 2. TERRY GROSS#That was probably really helpful. 3. JOHN GREEN#(Laughter), Yeah. And so if somebody had an X-box, I would play video games withthem. 4. TERRY GROSS#Did it feel awkward to be 22 and trying to help parents through a period when their child was, you know, dying or possibly dying? Did you feel like, who am I to
(9) 1. WATTERS#Right, naturally. And Juan brings up an interesting point. So NBC had no problem leaking the access Hollywood tape to the Washington Post, but according to Ronan Farrow, maybe had sat on this explosive story about Weinstein, Greg. 2. GUTFELD#Yeah. The funny thing is Trump is basically making the same – in that videois making the exact observation that we are making. If you’re in a position of power you can grab whatever. Anyway, I came up with a theory – I did n’t nish it. I came
(10) 1. KJ APA (10:56:44): I know. I know. 2. KATHIE LEE GIFFORD#56:45): Do you ever do anything about the nostril hair? Never mind. 3. KJ APA (10:56:49): No, but they dye my eyebrows, as well. 4. KATHIE LEE GIFFORD#56:51): Yeah, we can see. 5. HODA KOTB- (10#56:51): They do. 6. KATHIE LEE GIFFORD#6:51): It’sa lovely matching, yeah. KJ APA(10:56:53): Yeah. Yeah.
189 7. KATHIE LEE GIFFORD#56:54): How long did it take to get used to that, that look for you
(11) 1. … we had upwards of a hundred men sending us threats. Weactually had to have police at our o ce all week. 2. GUY RAZ#Wow. 3. WHITNEY WOLFE#They were emailing my employees, calling my employees, call- ing me, leaving the most vulgar messages. 4. GUY RAZ#Because you’re self-proclaimed feminist organization. 5. WHITNEY WOLFE#Yeah, female empowerment is not something that they believe in, unfortunately. So,you know, we get this too. The bullying - now I don’t let - that does n’t hurt my feelings anymore because I know these people are just atrocious. But it fuels me. The anger
(12) 1. GUY RAZ#OK. So I want to ask you rst about this idea of information asymme- try because, basically, your argument is that without pay transparency, the power dy- namic is heavily weighted in favor of the employer - right? - because they’ve got more information. 2. DAVID BURKUS#Yeah, exactly. So - and they have - I mean, it’s sortof like - it used to be this way in buying cars, and that’s why we we’re all sort of so worried. But now in an age of transparency of information, you can know everything
(13) 1. … he’s the secretary of state of the United States. It’s - he’s pretty frustrated with that, that he feels like he ca n’t do his job. 2. TERRY GROSS#One o cial told you, the only reason why Tillerson has stayed this long is loyalty to the country. 3. DEXTER FILKINS#Yeah, you know, he’s an Eagle Scout. And there’s a lotof Eagle Scouts in the president’s cabinet, and there’s a lot of generals around him. And some- body said to me, the only people lef around the president are generals and Boy Scouts. And they
190 (14) 1. …They should get an allowance regardless of what they do. 2. KATHIE LEE GIFFORD#04:01): Hmm. 3. HODA KOTB- (10#04:02): And it will help them learn to manage money and things like that. Chores, should just be part of what they do without getting paid. 4. KATHIE LEE GIFFORD#04:07): Yeah. If they’re – if they’re getting the great bless- ing of being ina home and having there everything provided for them, then something should be required of them. 5. HODA KOTB- (10#04:15): Right. 6. KATHIE LEE GIFFORD#04:15): My kids had to clear o f the table af er –
(15) 1. … the next couple of years will help doctors and lawmakers decide how to use this technology responsibly. Now, I’ve got to say I would do it all things considered. 2. GAYLE KING#Yeah. 3. CHARLIE ROSE#Absolutely. Knowledge is power. 4. GAYLE KING#I agree with that mom, knowledge is power. 5. CHARLIE ROSE#Yeah. And – and the more they are nding out, the more possi- bilities thereare to miss certain diseases. 6. GAYLE KING#Right. 7. CHARLIE ROSE#Sickle cell anemia, for example – 8. GAYLE KING#Right. 9. CHARLIE ROSE#– is one those that they’re nding out a lot about. 10. TONY DOKOUPIL#Mm-Hm. 11. CHARLIE ROSE#How you can
(16) 1. MARA LIASSON#Thank you. 2. STEVE INSKEEP#That’s NPR’s Mara Liasson. 3. STEVE INSKEEP#OK, so the president seems open to at least temporarily undoing his executive order on Obamacare. But he’s not at all happy with courts blocking his travel ban, which happened again.
191 4. DAVID GREENE#Yeah. for the third time, President Trump’s travel ban has been put onhold. The latest version of the ban barred travelers from eight countries, six of which are majority Muslim. This, of course, led to lawsuits. And among those who sued was the state of Hawaii
(17) 1. CARSON DALY- (08#22:52): Af er Selena paused for the applause from the crowd, she continued with her speech as she thanked her friends and family and for all those support this past year. I know you interviewed them both. 2. SAVANNAH GUTHRIE- (#23:01): Yeah I mean, there’s nothing surprising about that moment. Those two womenare such close friends. And, I mean, Selena’s grati- tude to Francia is endless. 3. HODA KOTB- (08#23:10): Yeah. 4. CARSON DALY- (08#23:10): Yeah.
(18) 1. SAVANNAH GUTHRIE- (#42:20): Putting the steal in Steals&Deals. I like that. Ooh, nail polish. These are cute little manicure sets, too. 2. JILL MARTIN- (08#42:23): Yeah these are great for all ages. The NCLA nail polish. The retail, sixty-four dollars. Now, it’s a ve piece toolkits, the pouch and the two lacquers so you get everything you see here with the pouch. And I like that they’re themes. So Troublemaker
(19) 1. ANTHONY BOURDAIN#No way, Philly? 2. ROBERT LUCAS#Oh, yes, for sure. 3. ANTHONY BOURDAIN#Wow, that’s treason. Do they, like, change the plates on their car and, like, wear a disguise ? I mean... 4. ROBERT LUCAS#It’s di ferent. The poppy seeds help. 5. ANTHONY BOURDAIN#Yeah, and I like this roll. It’s awesome. That’s delicious. Well, we’ve learned something here today. Jersey cheesesteaks - I’m not saying they’re better than Philadelphia - yeah, I am, actually, so there. This is great. 6. ROBERT LUCAS#Glad you enjoyed
192 (20) 1. … passport, her birth certi cate, her child’s passport and she was staying at a friend’s house in Upstate New York. So prosecutors believe because she has some wealth that she is a ight risk. 2. VLADIMIR DUTHIERS#All right. 3. GAYLE KING#Once again I’m intrigued. Very good tease. 4. VLADIMIR DUTHIERS#Yeah, I’m intrigued.
(21) 1. … out t I was wearing was, like, a knee-length dress with a cardigan and tights and boots. I just never wore that again. And I did n’t wear any form- tting clothes af er that. 2. MELISSA BLOCK#Younever wore it again to work because he had commented about you in it. 3. UNIDENTIFIED WOMAN##Yeah. And where I’m coming from is, like, OK, if thesystem is n’t going to help me, what can I do to help myself? And this is what I have control over - is how I dress and ways in which I present myself. 4. MELISSA BLOCK#She says
(22) 1. …or I don’t want to be with just one woman. And your life, for years, was kind of an echo of those songs. But you wrote about that kind of life in your songs in such a di ferent way than all the other people who wrote about that. 2. LOUDON WAINWRIGHT-#Yeah, I was - I had a chaotic, you know, rambling life.And it was interesting. And it was the life that I chose, but I did n’t feel good about it. I mean, I did n’t have a lot of bravado, you know? It was
(23) 1. ROBERT SMITH#It sounds insane. 2. STACEY VANEK SMITH#(Laughter)Yes, it does. 3. ROBERT SMITH#And you have to love the folks at Genscape because they listened to this, and they treated it like it was the most normal idea in the world. 4. JOSEPH SPELINKA#Yeah. Probably, we can look at a di ferent part of the visible spectrum ora di ferent part of the light spectrum to see growth quality the same way they do for corn and other crops in the United States, yeah. 5. ROBERT SMITH#What else can we do? Count trucks - avocado trucks
193 (24) 1. But in the things that were most important in life I of en went to her in my darkest moments and she was the one that had the wisdom and the – and the guidance. And so we both learned a lot in this experience. 2. KATHIE LEE GIFFORD#30:32): Yeah, it’s a beautiful book. And Hoda and I are both very,very close to our sisters. I ca n’t imagine being at odds with her. 3. HODA KOTB- (10#30:38): Right. 4. KATHIE LEE GIFFORD#30:38): She’s such a life force in our lives. 5. IDINA MENZEL- (
(25) 1. …of a screaming pig and live with that much more comfortably than I did the rst time. And I can lie and say it tormented me forever and since, but, you know, I felt that ugly emotion or lack of it, and I thought I should mention it. 2. DAVE DAVIES#Yeah. You said, I did it this time without hesitation or remorse. 3. ANTHONY BOURDAIN Yeah. 4. DAVE DAVIES#Butit was a relief when the screaming stopped. 5. ANTHONY BOURDAIN#Well, yes, no one - no good person likes to hear or see an animal in pain. That is monstrous. I mean
(26) 1. …obsessive detective that we have in the popular imagination. Obsessiveness is of en linked to this, like, genius of observation that just is not my experience at all. Like, I nd that my OCD makes me a terrible detective. 2. TERRY GROSS#Because you’re focused on the wrong thing obsessively. 3. JOHN GREEN#Yeah because I can’t notice the world outside of myself in the way that Iwant to because I’m so deeply and irrationally focused on stu f that’s happening kind of within me. 4. TERRY GROSS#So the characters in your new book are also dealing with the deaths of parents. And in The
194 (27) 1. JOHN GREEN#Yeah. 2. TERRY GROSS#So - and I really just kind of sympathized with her and admired how hard she was trying to have the right amount of connection with her daughter without being too pushy or smothering her or being overprotective. 3. JOHN GREEN#Yeah, it’s really hard. it’s so - you know, parenting ateenager - I can only imagine how hard it is. The great thing about having little kids is that at least you can solve their problems, you know? At least you know how to change a diaper
(28) 1. In fact, what is interesting is, in the past 18 months or so, several states have actually passed state laws that forbid companies from asking prospective hires what your salary at your previous job was. They’re trying to actually reduce the information asymme- try. 2. GUY RAZ#Yeah because if they ask you that, you tell them the truth. In theirminds, they might be thinking, oh, well, that’s awesome. That’s like 20 percent less than we pay. 3. DAVID BURKUS#Yeah, no. When I was teaching full-time, I used to tell
(29) 1. …more with one person and less with another person. You know, you’re more quiet and introverted with one person than another. So, like, did your parents change when they became couples with di ferent people , and did you change when you became part of those di ferent units? 2. NOAH BAUMBACH#Yeah, I’m sure they did. And I mean, I observed it,and I’m sure they did in ways that at that time I did n’t even see. I mean, I remember, like, the rst Christmas af er they separated and both of them got these big,
(30) 1. DEIRDRE ALPHENAAR#Wehave been described as the James Bond of commodi- ties and energy markets by The Wall Street Journal, no less. 2. ELIZABETH KULAS#Oh. That’s a pretty good pull quote . That’s the pull quote you want from The Wall Street Journal, 3. DEIRDRE ALPHENAAR#Yeah. So that was pretty exciting. 4. ROBERT SMITH#Deirdre explains that in the nancial world, everyone wants an edge - a little piece of data that no one else has. And Genscape sells them that data.
195 According to public documents, the company brings in around a hundred million dollars a year
(31) 1. …at the time, they were focused on one app, which was a consumer loyalty app called Cardify - kind of like a digital punch card for your favorite co fee shop, for your favorite whatever it is. 2. GUY RAZ#And he just told you about this at this dinner party. 3. WHITNEY WOLFE#Yeah, we were just at a restaurant having dinner, just a few of us. And I said, well, that sounds really interesting. And when he asked what I was doing, I said, you know, I’m really looking for a job. I could probably help you
(32) 1. … like bars or clubs. It was all ages, people sitting, drinking warm beverage, listening to singer-songwriters. 2. OPHIRA EISENBERG#Yeah. I mean... 3. JASON MRAZ#It was... 4. OPHIRA EISENBERG#And this is before... 5. JASON MRAZ#Perfect. 6. OPHIRA EISENBERG#... Youhad to, like, promote yourself on Instagram or what- ever. 7. JASON MRAZ#Yeah. We did have the Internet. 8. OPHIRA EISENBERG#Sure. 9. JASON MRAZ#But... 10. JASON MRAZ#But itwas very basic. I mean, you had to hook it up to a phone line, but it worked. And I did have to hand out yers. So it did require real legwork. Even- tually,
(33) 1. …is undercutting these other strong, proud African-American men and using the most racist images to do so. So that was Ali, you know? There was - there were no easy
196 answers. He was very complicated in these ways, and he didn’t really care about the contradictions. 2. DAVE DAVIES#Yeah, and some of these opponents admired him until he worked out on them verballylike that. Washe tougher verbally on black opponents than white ones? 3. JONATHAN EIG#That’s another one of the great puzzles. He absolutely was. He showed more respect for his white opponents. It’s almost
(34) 1. KEIR SIMMONS- (08#35:29): – in many ways. The same kinds of struggles. 2. VANESSA KIRBY-(08#35:31): Yeah. 3. KEIR SIMMONS- (08#35:32): She is royal but doesn’t get to be the boss. 4. VANESSA KIRBY- (08#35:35): Yeah, which she hates. Because she’s naturally, you know? 5. KEIR SIMMONS- (08#35:38): And she is naturally the boss. 6. VANESSA KIRBY-(08#35:39): Yeah, naturally the boss. She’s a boss. (Excerpt from The Crown) 7. KEIR SIMMONS- (08#35:42)
(35) 1. …I was just a product of the culture of the’ 60s and’ 70s. But what’s really been most disturbing is liberal Hollywood, who has been very aggressive about promoting femi- nism, promoting the rights of women, and here was someone who publicly promoted it and practiced hypocrisy. 2. ROBINSON#Yeah, and apparently there have been stories for years and years and years, fordecades, about this guy Harvey Weinstein. For the record, there was not a time when what he did was acceptable, right? 3. TODD#Let’s get that done, yes. 4. ROBINSON#I mean, –
(36) to know a person and knows who they are and how they tick and – and has a personal relationship with them, it’s a totally di ferent story.
197 1. GAYLE KING#Okay. 2. NORAH O’DONNELL#Can I ask you about President George W. Bush spoke yes- terday and he said bigotry seems to be emboldened? 3. REPRESENTATIVE PAU#Yeah. – well, I mean, I didn’t hear his speech but iden- titypolitics has gotten out of control in this country. And identity politics is being played on the lef and the right. it’s really dangerous for our country. And what iden- tity politics does is it seeks to
(37) 1. …have done worst things and failed at Oxford, right as a Rhodes Scholar. 2. KRISTEN HADEED- (09#44:32): Yeah. 3. MEGYN KELLY- (09#44:33): Thank you both. 4. RACHEL SIMMONS- (09#44:34): And I’ve done many, many things since. 5. MEGYN KELLY- (09#44:36): Yeah, sure, including write this book. 6. KRISTEN HADEED- (09#44:37): Yeah. 7. MEGYN KELLY- (09#44:38): Thank you for coming on and talking about it openly. Ladies, thank you. (09:44:42): Coming up, we’ve got CMA Award winner and Grammy- nominated country star Lee Brice
(38) 1. … has access to psychiatric help. But, that all of those people that saw that trauma wo n’t necessarily think that they need to seek out help. And he was hoping we could all spread the message that if you were one of those people, you should de nitely do that. 2. WATTERS#Yeah. Very traumatizing experience. All right. Our Democrats and Re- publicans close to anagreement on a new gun control measure following the massacre? An update next. 3. PERINO#Democrats and Republicans in congress have long sparred on the subject of gun regulation in America, but could they be nearing an agreement
(39) 1. …had been before. 2. UNIDENTIFIED WOMAN##Yeah. And so my perception of it, even though I doubt that this is what went into it - but my perception was like, oh, this feels punitive just for reporting something (laughter).
198 3. MELISSA BLOCK#Punitive to you, not to him. 4. UNIDENTIFIED WOMAN##Yeah. And so that was a bummer because I was there for the next yearand a half. And it changed the way that I trusted the system. And it changed the way that - you know, if I saw him cleaning outside, I would just go all the way around
(40) 1. GREG BAXTROM#Thanks. 2. ALEX WAGNER#It tastes even better than the grape juice I remember. 3. GREG BAXTROM#Yeah. 4. ALEX WAGNER#Greg, I read – I read that that you got some culinary training. Well, obviously at some of the greatest restaurants in the world, but also in the Boy Scouts. 5. GREG BAXTROM#Yeah. I was in the Boy Scouts for – for really a long time.And, you know, in the beginning, you know, parents are doing a lot of the things for you. But as you get older, as you learn how to do things, you then are
(41) 1. …going to behave that way, and to have accountability. 2. SAVANNAH GUTHRIE-(#19:05): And it – 3. TINA BROWN- (08#19:05): Well,the cool factor is important, that it’sjust an uncool thing to be this kind of a jerk. 4. TED BUNCH- (08#19:09): Yeah. And it’s an invitation to men, not an indictment on manhood.It’s an invitation to do things di ferently. 5. SAVANNAH GUTHRIE-(#19:14): And to change our culture. 6. HODA KOTB- (08#19:15): Yeah. 7. SAVANNAH GUTHRIE-(#19:15): And it really starts with
(42) 1. …dollar. I’m totally getting that. 2. STACEY VANEK SMITH#Thatis a good day. 3. ROBERT SMITH#That’s a great day. 4. STACEY VANEK SMITH#Imean, yeah.
199 5. ROBERT SMITH#But then you come back the next day. 6. STACEY VANEK SMITH#Andthey’re, like, $3 apiece. Yes, all the time. 7. ROBERT SMITH#Yeah. And who knows why, right? I would love some data that wouldtell me how prices are going to change in the avocado market. Before we leave Genscape, I give this pitch to Deirdre and her colleague Joseph Spelinka. I have the perfect name for our project. The
(43) 1. …cheesesteaks - I’m not saying they’re better than Philadelphia - yeah, I am, actually, so there. This is great. 2. ROBERT LUCAS#Glad you enjoyed it. 3. DAVE DAVIES#That’s fun. That joint’s about ve miles from here. I’m going to get over there. 4. ANTHONY BOURDAIN#Yeah, it’s good stu f. 5. DAVE DAVIES#I’m going to get over there.Do you care about the reactions you get from the locals af er the episodes appear? 6. ANTHONY BOURDAIN#I care about the - yes. I - what I want to happen ideally - and it’s so weird. It
(44) 1. LIANA HEDGE (09:04:47): Yeah. And – 2. TAMARYN YODER (09:04:48): And then ten years af er me, Adam came. 3. LIANA HEDGE (09:04:50): Yeah. My – 4. TAMARYN YODER (09:04:51): We adored him. 5. LIANA HEDGE (09:04:52): Yeah. And he was ours. I mean, we – 6. TAMARYN YODER (09:04:55): Mm-Hm. 7. LIANA HEDGE (09:04:54): – he – we cuddled him, we held him. (Photo of Mary and Bill Yoder and kids)
200 (45) 1. …book. And my kids love it to this day. I read them that all the time. 2. TERRY GROSS#My impression is you had a happy childhood. You’ve said you had a happy childhood. 3. JIMMY FALLON#(Laughter) I really did. I know it’s odd for a comedian. 4. TERRY GROSS#Yeah, that’s what I was going to say. It’s odd for acomedian. So many comedians run on depression, anger... 5. JIMMY FALLON#Yeah. 6. TERRY GROSS#... Resentment and had, like, really di cult childhoods. Do you think that, like, your sense of humor was a fected by
(46) 1. … at this construction site. It’s a few blocks from my apartment. They’re building a new part for the hospital. And she says you can go back in time and gure out how quickly the construction crews are moving. 2. KATHERINE SCOTT#You can actually see that. That construction... 3. ROBERT SMITH#Yeah. You see, like - you can see they - those used to bebuildings there, and they took it out. 4. KATHERINE SCOTT#Yep. 5. ROBERT SMITH#And, you know, I mean, if I did some analytics, you know, I could use this data to gure out when they
(47) 1. are grilled. I like to slice them down the middle to get a little more char on there – 2. CARSON DALY- (08#49:14): I like that, too. 3. CARRIE MASHANEY- (0#49:15): – because that’s my favorite part of the whole- - 4. CARSON DALY- (08#49:16): Yeah. Youdouble the surface area of the dog and you get a nice,crisp bite on twice as much meat. 5. CARRIE MASHANEY- (0#49:20): Exactly. And the onions – 6. CARSON DALY- (08#49:21): You got the onions going down. 7. CARRIE MASHANEY- (0#49:22): Yep
201 (48) 1. and very problematic infection. And you’re also going to refer in the reading I’m about to ask you to do to the microbiome, which is the collection of, you know, bac- teria and microorganisms in the gut. And so the goal is to always have a healthy mi- crobiome. 2. JOHN GREEN#Yeah. I mean, one of the really weird things about being a person isthat about half of the cells inside of your body are not yours, they’re microbes. And that’s also something that is of some concern to Aza, and for that matter to me, to me.
(49) 1. always raise, which is, you know, can you descend into this underground, describe it and not glamorize it? That... 2. TERRY GROSS#But he kind of lived part-time in that underground. 3. ANTHONY DECURTIS#He certainly did . 4. TERRY GROSS#He used heroin, too. 5. ANTHONY DECURTIS#He did use it. 6. TERRY GROSS#Yeah, and he was the rst person to inject John Cale, his band matein The Velvets, with heroin. 7. ANTHONY DECURTIS#That is true. Yeah, (laughter) I mean, all of that is true. They - I mean, it never really was his particular drug of choice
(50) 1. actress. You like being other people. 2. HODA KOTB (10:27:37): Yeah. 3. IDINA MENZEL (10:27:38): Exactly. 4. KATHIE LEE GIFFORD (10:27:38): And putting yourself out there is a whole other thing, isn’t it? But this – 5. IDINA MENZEL (10:27:41): Yeah. And it was a way to bond together. See, I’m takingover. I’m going to let her speak. 6. HODA KOTB (10:27:46): I know. It’s still happening. 7. CARA MENTZEL (10:27:47): I remember – I remember saying – I remember saying
202 (51) 1. You like that? 2. CARRIE MASHANEY- (0#49:53): Yeah. 3. CARSON DALY- (08#49:53): Al, you cook a lot of dogs. 4. AL ROKER (08:49:55): I do. I’ve never put cream cheese but this is great. 5. CARRIE MASHANEY- (0#49:57): Yeah. And so with the jalapenos, too. 6. HODA KOTB- (08#49:59):Wait. Is that Philadelphia cream cheese? 7. SAVANNAH GUTHRIE-(#50:01): Oh. 8. AL ROKER (08:50:02): Oh. 9. CARRIE MASHANEY- (0#50:02): De nitely not, de nitely not. 10. BRIAN DUFFY- (08#50:04
(52) 1. Stars, the one where it’s written from the point of view of a teenage girl who has - is it stage 4, cancer? 2. JOHN GREEN#Yeah. 3. TERRY GROSS#So my understanding is that’sbased in part on one of your fans who actually was dying of cancer. 4. JOHN GREEN#Yeah. My friend, Esther Earl - she died of cancer in 2010 when she- was 16. And she was a really involved member of the community that grew up around the videos that my brother and I made and was really involved in our charity projects and became a friend of mine.
(53) 1. the appeal of being nonbinary and also the attraction of traveling around in the camper. 2. JAMIE SHUPE#But yeah, we went from the 3,000-square-foot house to the two- bedroom to the one-bedroom to the studio and - right? - which is almost to the pup tent on the beach (laughter). 3. SANDY SHUPE#Yeah, and I draw the line there. No, no - no pup tents on the beach. 4. SHANKAR VEDANTAM#And that’s how we leave them - Jamie owing like water and Sandy the level ground nearby. Debates over gender quickly produce vitriol, anger and contempt. The folks who think biology
203 (54) 1. a pleasure to meet you. 2. DANIELLE BRADBERY-#51:08): Thank you. 3. MEGYN KELLY- (09#51:09): All right. So this is the one like you – you did this one yourself. You’re in control. It sounds like you had something to say. 4. DANIELLE BRADBERY-#51:15): Yeah. I was actually able to be a part of writing this album because the rst album, I wasn’t. 5. MEGYN KELLY- (09#51:20): Yep. 6. DANIELLE BRADBERY-#51:20): So there is a lot that I wanted to gure out just as an artist and as a person, being so young
(55) 1. And he told everybody from the earliest possible age. This was not something that he cooked up when he became, you know, the Muhammad Ali. He always believed in himself somehow. 2. DAVE DAVIES#AndCadillacs and big houses were part of the dream, too. 3. JONATHAN EIG#(Laughter) Yeah, he talked all the time about these mansions he was going to build andhow many Cadillacs he was going to own. And he always talked about building housing projects and buying a house and living above the housing project. He had these very speci c dreams. And before certain ghts,
B.1.2 COCA ‘yeah’ + disagree (37 examples) B.1.2.1 COCA ‘yeah’ + disagree, accept belief (15 examples)
(1) 1. MAGGIE RODRIGUEZ: Welcome back to THE EARLY SHOW on this Tuesday morning. Weve got a fantastic crowd out here, including some kids and some folks from the Childrens Museum of Manhattan. My daughters favorite place on earth. So important to support your citys local museums, especially kids museums because kids have fun and learn a ton. 2. HARRY SMITH: Thats a – its a great place. And as your daughter gets a little older, it will transfer to the Museum of Natural History. 3. MAGGIE RODRIGUEZ: Yeah. She – shes kind of getting there already with the dinosaurs.
204 4. HARRY SMITH: Shes getting there already, right, right. 5. MAGGIE RODRIGUEZ: Im Maggie Rodriguez with Harry Smith. And coming up in this hour, it captured the countrys attention when Facebook founder Mark Zucker- berg made an enormous donation – a hundred million dollars – to help turn around the failing Newark, New Jersey, school system. Af er all the hoopla about that dona- tion, it comes down now to this man – the mayor of Newark,
(2) 1. I don’t even call myself a Mexican or a Latino. I usually call myself Zacatecano because my parents are from the state of Zacatecas. That’s my ethnic identity. But at the same time, of course I’m American. I was born in this country. I’m speaking English, of course I’m American. I don’t think there’s much of a question to it. 2. CONAN: And Ruben, the language barrier, obviously not unique to Latinos but nev- ertheless di ferent from a lot of Europeans who came here. 3. Mr. NAVARRETTE: Yeah, and it’s similar to a lot of Europeans, as well. You look at the German example. The rst group of people to be persecuted because of language were German-Americans. The rst victims, if you want to call them that, targets of English-only laws were Germans in the Midwest. And so, you know, that goes way back. And I think the interesting thing there is there was once a time where people believed that Germans and Italians and Irish and Jews would never assimilate
(3) 1. who’s given up guns because - like Sterling Hayden in Johnny Guitar and you’re riding into town. Like, you do n’t have a gun, but, like, you’re so tough and you’re so cool, you’re going to win no matter what anyways. 2. JIMMY FALLON#Yeah. I probably was the sidekick to the cool guy where I still was kindof cool, but in my head, I saw myself getting shot o f a horse, which again, when I go to therapy, something’s going to come from that. 3. JIMMY FALLON#But I do n’t know
(4) 1. we’re going to be the, you know, next big thing. And then shortly thereaf er, we started getting excited about this side project, Matchbox, which is what became Tinder. But at the time , it was Matchbox. 2. GUY RAZ#... Which was supposed to be a dating app.
205 3. WHITNEY WOLFE#Yeah, it was, like, a irting app. It was, like,connecting people to irt, right? And af er we were really unable to get Cardify to gain signi cant traction, we all - you know, the rst few of us were kind of like, maybe we need
(5) 1. troubling to me because, you know, what comes af er, if we do that? 2. TERRY GROSS#And it’s hard to rebuild something. This whole, like, network of treaties, and ambassadors and agreements, it’s hard to rebuild something like that if it’s torn apart. 3. DEXTER FILKINS#Yeah. I mean, you’re talking about - what you’re really talking aboutis American leadership in the world. If you do n’t have American leadership, who’s it going to be? Is it going to be Russia? Is it going to be China? And I - you
(6) 1. people who did similar drugs. And the people who were doing LSD and marijuana and other drugs, those were the people I wanted to hang out with. 2. DAVE DAVIES#You found a home in - among restaurant people , right? You dropped out of college, went to culinary school. 3. ANTHONY BOURDAIN#Yeah. Well, I started working as a dishwasher one sum- mer. And it wasreally a big event for me because up to that point, I was lazy. This was the rst discipline, the rst organization because it is a very militaristic organization, the kitchen brigade, the rst people
(7) 1. in the world, we’re all going to know about it. 2. DEIRDRE ALPHENAAR#That’sright. Yeah. We’re all going to know about it. And we’re probably going to know within the day. And this is just one supply chain. 3. ROBERT SMITH#It’s just oil. 4. DEIRDRE ALPHENAAR#Yeah. It’s going to be the same for all commodities. But it’salso going to be the same for how many new homes are being built, how much more tra c are on the highways... 5. ROBERT SMITH#Avocados. Soybeans. 6. DEIRDRE ALPHENAAR#... How much more avocados are being grown. Yeah,
206 (8) 1. I make $50,000. 2. DAVID BURKUS#SoI believe that inside of a company everybody should know what everybody gets paid. 3. GUY RAZ#Which is crazy. 4. DAVID BURKUS#(Laughter). You said it, not me. 5. GUY RAZ#This is David Burkus. He’s a professor who writes about leadership and business management. 6. DAVID BURKUS#Yeah. But if you asked my 5-year-old son, I write books, I givetalks, and I take care of him. 7. GUY RAZ#And David’s known for his ideas about salary transparency in the work- place. 8. DAVID BURKUS#Imean, it’s sounds - you’re right. It sounds crazy,
(9) 1. I go home. Its pork loin wrapped in prosciutto, you know, a lot of sage over the top of it. 2. ALEX WAGNER#Yum. 3. ANTHONY MASON#And our beverage this morning? 4. GREG BAXTROM#It’s a – 5. ANTHONY MASON#With a lovely color, I must say. 6. ALEX WAGNER#It’s beautiful. 7. GREG BAXTROM#Yeah. It’s like a – it’s Concord grape. So, it sortof like a Welch’s Grape Juice. It’s like – 8. ANTHONY MASON#Ah. 9. GREG BAXTROM#It’s an heirloom – 10. ALEX WAGNER#A real Concord grapes. 11. GREG BAXTROM#Yeah. Real Concord grapes. 12. ANTHONY MASON#You’re going back to
207 (10) 1. he does not represent the other 11 million individual who come to this country, they play by the rules. They work really hard. This guy is not one of them. And so to lump him in as we’ve seen folks on the right do with them is unfortunate. 2. CARLSON#Yeah. I don’t know if they’re living on the shadows. I have a lot of peo- ple here illegally on my show bragging about their status. I wonder if this is evidence that the Democratic Party has changed. Hillary Clinton actually criticized the city of San Francisco two years ago for i don’t really get what’s going on here
(11) have a model of it... 1. STACEY VANEK SMITH#No. 2. ROBERT SMITH#... Right here. 3. STACEY VANEK SMITH#Oh,it’s so cute. 4. ROBERT SMITH#Little Poddy (ph). 5. STACEY VANEK SMITH#(Laughter). 6. ROBERT SMITH#It’s about the size of a loaf of bread. 7. STACEY VANEK SMITH#With,like, solar cell wings. 8. ROBERT SMITH#Yeah, no. It’s got a little camera lens on it. So thatfeeling - that feeling you have right now, Stacey - that excitement about sending something you have touched into orbit. It has a name. And it’s a name I heard over and over again when I
(12) 1. don’t care what they do on their own time. But when they’re on the clock of the employer, when they’re wearing the uniform of their team. 2. MACCALLUM#They’re saying that the moment they can have the biggest impact because that’s when everybody is watching them. 3. HUCKABEE#Yeah. But is that the proper place for politics? I mean, let’s face it, Martha, there’s a time and a place for everything. And if they really are that interested in making political statements I’ve got a real good suggestion for them. Give up their multimillion
208 (13) 1. … coming said do you have an electric station at your home to charge my car? Is that what it’s come to now? 2. NICHOLAS THOMPSON#Well – so that’s – that’s, A, annoying, Gayle. 3. CHARLIE ROSE#I have an electric car and I have a charging station. 4. GAYLE KING#Yeah, but I mean, when you’re going to somebody’s house, I just saw, wow, I never heard a questions like that. 5. NICHOLAS THOMPSON#So that’s because we’re in a strange transition period where things like that happen. A couple of years from now there will be
(14) 1. And so then you feel like, you know - what? - I’m never allowed to have a rough day? I’m never allowed to be like, no, I do n’t want to take a picture with a fan? I just want a co fee. Or... 2. JASON MRAZ#Yeah, kind of. I mean, I learned early on that if I -let’s say - dis a fan or if I’m not in a good mood when I meet someone, they could be as proactive against me as they are supporting me. 3. OPHIRA EISENBERG#(Laughter) That’s true
(15) 1. Four people killed in the United States, 1,500? 2. BOOTHE#That’s also includes guns – that’s also includes gang violence. 3. HARF#OK. (CROSSTALK) 4. HARF#So it does n’t make it OK. 5. GUTFELD#You’re using a speci c term. 6. HARF#Yes, and I just de ned it for you. 7. GUTFELD#Yeah. But it’s a miscalculation. You’re encompassing. You’re using a speci c term. And you’re talking all these others. A little bit dishonest. 8. HARF#Whatever you want to call it, I do n’t think it’s acceptable that we’re the only developed country in the
209 B.1.2.2 COCA ‘yeah’ + disagree, accept QUD (22 examples)
(1) 1. an elevator story for us, give us a call. 800-989-8255. Email us, talknpr.org You can also join the conversation on our blog at npr.org/blogof henation. Nick Paumgarten is a sta f writer for the New Yorker, and he joins us from our bureau in New York. And thanks very much for coming in today. 2. Mr-PAUMGARTEN-1St: Hi, Neal. Thanks for having me on. 3. CONAN: And you said with your story about - your piece around the story of Nicholas White, a man who was trapped in an elevator for 41 hours. 4. Mr-PAUMGARTEN: Yeah. Poor Nicholas White went out for a cigarette break one night while working at Business Week. He was a production manager there. He went and had a cigarette, got back on the elevator and the thing stopped. And he spent the next - yeah, 41 hours, a full weekend, in an elevator. By himself. 5. CONAN: An event that we eventually nd out changed his life. 6. Mr-PAUMGARTEN:It did. Youknow, Nick White holds that it wasn’t so much being
(2) 1. going to nd out. And then later, bringing out the bright colors for spring. How to know what you should be wearing when it’s time to shed those bulky coats. But rst, these messages.’ 4031060 2. HODA KOTB, co-host: We had a busy yesterday honoring a friend of ours. 3. KATHIE LEE GIFFORD: Who didn’t even show up. Not by her own – not because she didn’t want to show up. 4. KOT 5. B: Yeah, Ann Curry was honored as Mother of the Year by the American Cancer So- ciety. She and a really wonderful breast cancer surgeon as well, Dr. Heerdt. 6. GIFFORD: Alexandra Heerdt. 7. KOT 8. B: Alexandra Heerdt. And... 9. GIFFORD: By the American Cancer Society, by the way, yeah. 10. KOT
210 11. B: Yes, what did I say? 12. GIFFORD: Yeah, that’s who – that’s who honored. 13. KOT 14. B: Yeah, cancer. And Kathie Lee ended up being the emcee of the evening, or the
(3) 1. I thought that was pretty remarkable that perhaps it is a lot more widespread. Chris Hayes who is a host at MSNBC said you have to wonder if Hollywood is staring at something that would be as big as the scandal that the Catholic Church went through 15 years ago. 2. GUILFOYLE#Yeah. And you had a good discussion about that today. The other thing, Dana, just to follow very quickly is Steph McFarland had said that he had heard about it or knew about it, was making jokes. At least there’s suggestion that there was some knowledge and discussion about “i wonder about this problem” “Yeah it could be a problem” - not agreeing or disagreeing?
(4) 1. Yes. And he could hear and feel my passion for this platform, and he said, you know what? You need to build this, but you need to do it in dating. And I said ... 2. GUY RAZ#(Laughter) Af er you said I’m not doing dating. 3. WHITNEY WOLFE#Yeah. And I said, I don’t think you understood me (laughter). I’m not going back into dating. Like, that’s not happening. And he said, no, no, no, this has to happen. What you’re trying to do needs to be
(5) 1. world you hear a rising alarm in their voice about what we might potentially be facing. 2. TERRY GROSS#You’ve been to Greenland, and you say you actually stood on land that you might have been among the rst people to stand on because it wasn’t - it was ice before. 3. JEFF GOODELL#Yeah, it was a very surreal experience. I was there with a scientist named Jason Box, and we were ying a helicopter over the Jakobshavn Glacier, which is the fastest-moving glacier in the world. And he spotted this bare spot of ice, and he said, we have to land
211 (6) 1. to crack down on immigration to prevent threats like this from coming into the coun- try. 2. ALEX EMSLIE#partially. But, you know, he also did n’t have any history of any kind of violent crime before Kathryn Steinle ’s death - some low-level drug convictions and deportation - illegal re-entry-related crimes. 3. RACHEL MARTIN#Yeah. So what’s been the reaction from the prosecution? And just more broadly, what’s the upshot of all of this now that a verdict has come in? 4. ALEX EMSLIE#Well, I mean, the prosecution was disappointed in the outcome in this case. They fought very hard and,
(7) 1. there mostly doing data entry. But it - Booklist reviews like 400 books every two weeks. And, you know, all those books were written by somebody. So that’s when I started to feel.. 2. TERRY GROSS#(Laughter) But were they read by anybody is the question. 3. JOHN GREEN#Yeah. Not all of them, certainly. But that’s when I started tofeel like, OK, well, being - it’s not quite like being a professional athlete or being an astronaut. Like, this is something that regular people do. Lots of people write books. And
(8) 1. TERRY GROSS#And it must be so - I do n’t have children, but it must be so inter- esting to have watched children who you did n’t expect to have become not only born but then, like, become full people and to see what that moment in your life led to. 2. LOUDON WAINWRIGHT-#Yeah. Now I have four kids, and they’re all formidable. You know, three of them are singers and talented - and songwriters. And very talented Rufus and Martha and Lucy are in the business and doing well in their own particular and interesting ways. And Lexie is just out
(9) 1. speech in the late 70s. He was always someone who was seen as – he was psychoana- lyzed by some of his critics. And so you can see a rapport as much as there’s a transac- tional play perhaps . 2. TODD#And by the way, he still hasn’t forgiven the Clintons. 3. COOPER#Yeah, I don’t know what to do with that. So, I...
212 4. TODD#There is a lot there. But it all comes down, he has not gotten over snubs from the Clintons. All right, guys, that’s all for today. Thanks for watching. We’ll
(10) 1. odd for you to, like, come talk and, like, be in public and have to talk because - I do n’t know. You’re just so used to - and you’re fantastic at your job. But, I mean, like it’s a di ferent beast. 2. TERRY GROSS#Yeah, I love the invisibility, yeah. 3. JIMMY FALLON#It’s a di ferent beast,but you did a bit with us. We played Pass- word, which is so fun. 4. TERRY GROSS#I totally unked out of Password. 5. TERRY GROSS#Like, I’m unking... 6. JIMMY FALLON#But that’s the best. 7. TERRY GROSS
(11) 1. underestimate our viewer ever. we like to do smart stories. We like to do important stories. And we – we always want them to get a better understanding of the world because of what we’re reporting. 2. CHARLIE ROSE#Talk about some of the correspondents you have worked with. 3. JEFF FAGER#Yeah. Some of the greats, really. It’s amazing. I mean,beginning with Don Hewitt, actually, because he’s a big part of this book and he’s a huge part of our story. It would n’t have happened without him. Just a creative genius. And
(12) 1. lot of money for some of this deadly equipment. 2. CHARLIE ROSE#I’m wondering how well he covered up his tracks. 3. BIANNA GOLODRYGA#Right. 4. CHARLIE ROSE#He seemed so meticulously prepared. 5. GAYLE KING#Yeah. 6. BIANNA GOLODRYGA#Yeah. 7. CHARLIE ROSE#And he also prepared so that people couldn’t nd things that helped him.
213 8. GAYLE KING#Yeah. A lot of questions still. The two biggest U.S. automakers are making ahuge bet on electric cars. Nicholas Thompson is here in our Toyota Green Room. Ahead – there he is, hello, Nicholas Thompson. 9. NICHOLAS THOMPSON-#Good morning. 10. GAYLE KING#How GM and Ford are racing to roll
(13) 1. in the hospital, you know, trying to watch over their father and deal with the doctors and the nurses and all the medical issues you have to deal with when someone’s in the hospital. Had you had a hospitalization experience with someone who you’re close to or yourself? 2. NOAH BAUMBACH#Yeah. I mean, I’ve - that experience was, you know - insome ways came from my own experience being in the hospital - I mean, being with some- body who is in the hospital and, at the time, I mean, that just horribly vulnerable feeling. But I
(14) 1. if it all falls together. MICHELE KELEMEN#That’s right. 2. RACHEL MARTIN#NPR’s Michele Kelemen - thanks so much, Michele. 3. MICHELE KELEMEN#Thank you. 4. RACHEL MARTIN#So we’ve got a verdict in a case that had become a focal point in President Trump’s e forts to crack down on illegal immigration. 5. DAVID GREENE#Yeah. So we’re talking here about a month-long trial that ended yesterday with anot-guilty verdict for an undocumented man in the death of Kathryn Steinle in San Francisco. Here is defense attorney Francisco Ugarte speaking af er the verdict. 6. FRANCISCO UGARTE#From day one, this case was used as a means to
(15) 1. editor of Bloomberg Businessweek who helped to compile the list. Bret, thanks for joining us. 2. BRET BEGUN- (Bloomb#Yes. You’re welcome. 3. ALEX WAGNER#Let’s start with a man who is on many lists, but that does not di- minish his reason for being on this list, Je f Bezos.
214 4. BRET BEGUN#Yeah, Je f Bezos. It’s hard to argue that he didn’t have amassive year. On June sixteenth of this year, decides to drop 13.7 billion dollars on Whole Foods. Really, what he did in doing that is essentially signal to the world that he does n’t want Amazon
(16) 1. And there – thre she is, the Queen. (08:36:47): And some unexpected challenges for Matt Smith, who plays Prince Philip, the Duke of Edinburgh. (08:36:52): You have a special kind of walk that you do that is the Duke of Edinburgh – 2. MATT SMITH- (08#36:55): Yeah. Well, he – he walked with his hands – 3. KEIR SIMMONS- (08#36:59): Right. 4. MATT SMITH- (08#36:59): – behind his backs. It’s like Danny from Grease, so. Yeah, I – I just love that he sort of lives by his own rules
(17) 1. a lot of war, you know? I mean... 2. TERRY GROSS#He’s seen a lot of war, right. So do you have any idea what kind of war he’s envisioning if we do go to war with North Korea? And I hate to even utter those words. 3. DEXTER FILKINS#Yeah, God forbid. there’s a lot of di ferent options. And, I mean, I’ve had some discussions about what those options are. they’re all terrible. that the easy scenario to imagine - I mean, it’s a terrible scenario - is the moment the United States
(18) 1. most afraid of contaminating you? 2. JOHN GREEN#I mean there’s a - yeah, I’m being super intentional about not saying that (laughter). 3. TERRY GROSS#Oh, OK, OK. 4. JOHN GREEN#So yeah, that ’s the... 5. TERRY GROSS#That’s ne. I don’t want to... 6. JOHN GREEN#Yeah. That’sthe only thing that I can’t. If I talk -I ca n’t talk directly about it because I get squirmy. 7. TERRY GROSS#You get what, squirmy?
215 8. JOHN GREEN#Yeah. 9. TERRY GROSS#Would it be awkward, too, for us all to know? Like, say we met
(19) 1. We are back, eight forty. What a week of Steals& Deals and this is the grand nale of the gif guide. TODAY contributor Jill Martin is back. And the best is last, gif s everything we’re about see under twenty dollars. 2. JILL MARTIN- (08#40:45): Yeah. I mean, I feel like everyone has so many people on their list and a budget. So we want it to be able that you can cross o f everyone on your list. So twenty dollars and under and free shipping on all. So this is the price. 3. SAVANNAH GUTHRIE-(
(20) 1. Thomas Gunderson, later on the Facebook page, I will never lie down when the pres- ident of this great country comes to shake my hand. And he’s going to be on Hannity tonight, so hopefully everybody checks that out. Do you have something you want to add? 2. GUTFELD#Yeah. I just wanted to add, we’ve talked about the rst responders andwe’ve talked about a lot of the brave citizens. The one thing we do n’t talk about are the dispatchers which are the police, re, and hospital dispatchers that are just inundated with information and misinformation
(21) 1. that’s available. So this whole thing is kind of a di ferent experience for me, and I - so every now and then, if someone brings it up or anything about kids books or something that, I can go, oh, yeah, I have a kids book. 2. TERRY GROSS#Yeah, but you had - you were on Shaquille O’Neal’s lap while... 3. TERRY GROSS#... He read your... 4. JIMMY FALLON#With that... 5. TERRY GROSS#How did you come up with that idea? 6. JIMMY FALLON#I did n’t know he wanted me to have him on his lap. I... 7. TERRY GROSS#You did n’t
216 (22) 1. And sometimes, that means working against the U.S. foreign policy best interests. Did you talk to Tillerson about that when you had a chance to talk with him? And did you get a sense that he’s trying to change his - the lens through which he sees the world? 2. DEXTER FILKINS#Yeah, a little bit. what’s amazing about Exxon - you know, it’s one of the world’s most successful corporations. It goes all the way back to Standard Oil, you know, decades ago. It is an enormous company. its annual revenues are close to $400 billion
B.2 LDC ‘yeah’ (93 examples)
This section includes 67 examples with agreeing followup content and 26 with disagreeing con- tent. Of these 26, f een were coded as accepting belief while rejecting at-issue content, and eleven were coded as accepting the QUD.
B.2.1 LDC ‘yeah’ + agree (67 examples)
(1) 1. B: i used to use a lot of credit cards i guess for a while i would use you know a variety of the the Visa and the MasterCard and the stores but 2. B: i think i i impulse buy too much with them or i buy things you know i see it on sale and i think oh it’s on sale i have to get it 3. B: and i really don’t need it or i really haven’t budgeted for it so um the last couple of years i really have tried not to use them at all what about you 4. A: Yeah 5. A: well we have this philosophy we use it when we go o f somewhere 6. A: but we pay for it as soon as we come back you know as soon as we get our bills we pay it o f and we only have one we tore all the rest of them up
(2) 1. B: i think that American manufacturers are getting a lot more 2. B: um
217 3. B: pressure to compete in quality than maybe we saw you know ten or twelve f een years ago 4. A: um-hum 5. B: that they’re they’re feeling the heat you know you see a lot of you know now uh you know the ads that come out they’ll say you know ninety percent of our products you know made in the USA 6. B: you know that kind of thing we buy we buy American so you can too you know kind of slogans 7. A: um-hum 8. A: yeah 9. B: but uh so it must be a heightened you know awareness that way the uh 10. B: um 11. B: one thing i’ve uh well i jus- 12. B: i don’t know how to say that cohesively so i’ll pass on that thought until it comes back in my head clearer 13. A: Yeah i think probably they’re having to change some of their strategies due to sur- vival in a lot of situations uh i think the car industry is a good example of that
(3) 1. B: we had more one-on-one since we seemed to know everyone on campus and we had a broad spectrum of languages and cultures and 2. A: hum 3. B: and backgrounds and so forth so i thought it was wonderful to be a bigger sh and not so much a number um 4. A: hm 5. A: um-hum 6. B: one of f y thousand i was one of three thousand and and you know di ferent per- spectives i uh but it sounds like we were both kind of looking at the same sort of sort of thing 7. A: right 8. A: um-hum yeah
218 9. A: y- Yeah i i could see i could see that point i could see how it would be i’m sure your classes were a lot smaller um because like we would have biology classes that did main courses that everyone has to take i mean hundreds of students in this class so actually yes you were a number
(4) 1. B: people aren’t tuned in to like saving for it and then buying it they just put it on a charge card put it on account and then pay o f the bill because they want it now it’s always have to have it now 2. A: Yeah um well it didn’t hurt you did it to i mean you didn’t go out and charge a whole bunch and lose everything did you
(5) 1. A: um yeah we had cat you know my family had cats when we were growing up the whole time until my mom developed an allergies so i mean i’m used i’m used to having cats around i like them 2. B: Yeah i like having ca- cats around or pets around in general i- i favor dogs over cats actually but
(6) 1. A: but um i could not charge to the limit and knowing that i was going to have to pay this thing out for ever and maybe su fer from having to pay out 2. A: but that’s just the way i feel about it 3. B: Yeah i think it’s it’s somewhat a um symptom of our 4. B: me generation and that we think we need all these things
(7) 1. B: you know even young couples buying a home they’ve lived in apartments that 2. B: provide those things so they just assume they’ll be in a home 3. A: Yeah that’s true
(8) 1. B: what kind of a dog is he 2. A: he’s part golden lab and part uh let’s see Alaskan no not Alaskan i always forget uh Australian shepherd 3. B: Yeah oh boy he’s a big one then huh 4. A: and yeah he’s pretty big that’s why i- it really surprises me you know that
219 (9) 1. B: well you know that animals are like that they like to roam and investigate and 2. A: Yeah i guess that’s yeah i guess that’s probably true
(10) 1. B: well the han- in the hands of the wrong person the car can be just as deadly as a weapon as a as a pistol 2. A: Yeah well i’m a bicycler and that scares me too to tell you the truth because i’ve been run o f the road and all sorts of things
(11) 1. B: well now down here we’ve got you know collection bins in the parks and things but there’s no 2. B: you know there’s absolutely no 3. A: Yeah there used to be uh a big uh trash bin several trash bins in front of the Wal- Mart in town here that uh you could put plastic jugs on one side and newspapers on another and glass on the third
(12) 1. B: well i i’ll i hope you’ll nd him you most likely will 2. A: Yeah we keep you know they they told us like at the Humane Society and the adoption center you know don’t give up hope you know and then
(13) 1. B: well for not summer crops summer crops it’s uh it’s really not time to plant okra not quite the ground isn’t warm enough but uh 2. B: some of those 3. A: yeah no i uh i put down black plastic 4. B: um-hum 5. B: um-hum 6. A: to help heat it up you know put it on the radishes and spinach but spinach kind of bolts quick on us so we put it Swiss chard instead yeah 7. B: Yeah right get that out early um-hum um-hum 8. A: so anyhow that’s what we’ve done for our house we’ve lef it all natural so there’s no upkeep you know it’s white cedar shingle
220 (14) 1. B: well a lot of companies are are coming down in price and i think that the insurance companies are starting to give uh bene ts 2. B: you know re- reduction in in medical costs if you know they 3. A: Yeah yeah it’s got to help them too 4. B: yeah so i think everybody will will bene t from the program 5. A: yeah i do too that’s right
(15) 1. B: well a lot of companies are are coming down in price and i think that the insurance companies are starting to give uh bene ts 2. B: you know re- reduction in in medical costs if you know they 3. A: yeah yeah it’s got to help them too 4. B: Yeah so i think everybody will will bene t from the program 5. A: yeah i do too that’s right
(16) 1. B: well a lot of companies are are coming down in price and i think that the insurance companies are starting to give uh bene ts 2. B: you know re- reduction in in medical costs if you know they 3. A: yeah yeah it’s got to help them too 4. B: yeah so i think everybody will will bene t from the program 5. A: Yeah i do too that’s right
(17) 1. B: uh you probably have seafood a lot also 2. A: Yeah they do oddly enough even being ne- near the gulf uh they tend to advertise the the main
(18) 1. B: uh it’s a ba- i mean it’s the basic premise with you know drug testing 2. A: Yeah 3. A: that’s right
221 (19) 1. B: uh he’s really a fectionate he likes to lay next to you and have you scratch his head and 2. A: huh 3. B: and and snuggle up to you and but if he’s not in the mood you’d better stay away from him so but dogs seem to be always in the mood they’re always you know right there and like like you to pet them and loving and so 4. A: Yeah that’s true i i like the way they come up and they’ll
(20) 1. B: they’re both females 2. A: Yeah both females they’re both spayed or whatever it is neutered
(21) 1. B: that’strue if if it’son the credit card it doesn’t seem like it’smoney out of your pocket and sometimes you may think well i don’t have the money so you use the credit card it’s like fake money or something 2. A: Yeah and it gives you a sense um a false sense of security or something it seems like because you say oh well i don’t have to pay for it now but you’re going to have to pay for it and it seems like
(22) 1. B: right well we seem to agree on the jury thing and uh but uh we’ll need an unanimous verdict and maybe let the judge have a opportunity to make the sentence yeah 2. A: Yeah 3. A: i think we should try it like you say if it doesn’t work we can always come back but i think it would be a good idea to try it
(23) 1. B: right well do you have crabs and lobster in that area that’s 2. A: ye- Yeah yeah crab and lobster and and uh
(24) 1. B: people i think probably prefer natural gas for for heating 2. A: Yeah uh-huh i would too because me i like gas heat i love it
222 (25) 1. B: oh i’m i’m a dog person i had i guess it depends on what you had when he were a kid right 2. A: Yeah probably i was allergic to dogs when i was a kid in fact i may still be um we
(26) 1. B: mandatory drug testing we’ll reduce your insurance costs by you know twenty per cent and of course you know the company’s not going to reduce the employee’s cost of insurance twenty per cent they’re going to reduce their own 2. A: uh-huh 3. A: no no but i- it’ll save in some areas yeah right 4. B: Yeah and that’ll cover it and maybe some insurance companies might start o fering it as a uh 5. B: as part of the package
(27) 1. B: it’s uh you know it all starts to become similar there’s you know there’s bene ts and there’s things that weren’t as good but as an overall package i’d say i i’m pretty happy so far with the way it’s turned out 2. A: Yeah 3. A: good good well hasn’t been a long time TI or i i’m
(28) 1. B: it’s the same principal when there’s a drought they ne you for using water you know they just 2. A: yeah 3. B: you don’t do what they say and you 4. B: not that i promote over government or anything but you know the the world’s in a bad enough state that 5. B: you know i 6. B: people being the way they are you kind of need some incentive 7. A: Yeah it would de nitely help you know even if they were to uh uh pay like for recycling aluminum or
223 (29) 1. B: it was like a dead end on the other 2. B: on the other end of the phone 3. A: oh was that yesterday 4. B: yeah uh-huh 5. A: oh yesterday i wasn’t home my friend picked up 6. B: oh yeah 7. A: so i didn’t know what it was yeah yeah yeah 8. A: but i like i what was it do you know what the day before it was 9. B: no uh-huh i just started um 10. B: actually yesterday 11. A: Yeah oh you started yesterday 12. B: yesterday is my rst day
(30) 1. B: if you look at uh some countries you expect them to have problems Latin America now with cholera and and a bunch of other problems associated with 2. A: um-hum 3. B: waste management 4. B: the United States you can expect more out of but we still have the same very deep problem of how to take care of our waste we create a bunch now now what do we do with it 5. A: Yeah now that now that it’s getting harder to get permits for land lls uh the the problem’s pressing down harder than ever
(31) 1. B: i thought this whole switchboard thing was shut down i was surprised to get the call 2. A: Yeah so did i but i thought i’d try one more time and sure enough it’s okay
(32) 1. B: i think all around it it’s going to help out a lot course 2. A: Yeah i do too
224 (33) 1. B: i imagine uh in in Texas there would be uh a lot of steak restaurants is that right 2. A: Yeah there seem to be quite a few quite a few
(34) 1. B: i have trouble with the chemical plants and things like that i just uh i think they put o f so much and they’re not regulated enough 2. A: Yeah that’s me too af er um on channel thirteen uh they had some
(35) 1. B: he said something about if you can win a million dollars would you not talk to your best friend anymore or something like that 2. A: oh would i would you give up your friend for money is that what it was 3. B: i think that’s the bas- what the basis of it was 4. A: ah there isn’t enough money for in the world for that 5. B: Yeah and i and i totally agree i don’t think i would give up i would give up my friendships my best friend for that no ’cause once all the money’s gone then ah then you just have yourself your your friend will always be there for you yeah 6. A: no not for money 7. A: (( not for money ))
(36) 1. B: and uh you know Texins is is not uh well i don’t think it’s 2. B: really directly supported by TI i think it has it’sown kind of funding but something like that that you know you would have to be a TI or a Texins kind of um does it 3. A: Yeah i think that would take care of any questions about competency for people running the show
(37) 1. B: and then she grew to be you know i had pictures of her when she would sit on my hand and then she grew to be pretty big you know like a pound or something i don’t know how much and i had her for over two years and she was very a fectionate she would you know 2. A: uh-huh
225 3. B: crawl on me and she would sit like on my neck or my shoulder while i was working and things like that yeah much more than you would think 4. A: Yeah i mean you know if you get them young and everything before they go kind of nuts so yeah rats are not my favorite animals in the world but i could see getting one from birth and everything 5. B: yeah i sort of weird for me to have had one too but there it was a convenient little pet to have because it stayed in it’s cage and you know it was easy easy to take care of and but 6. A: yeah i- i i have friends with hamsters and gerbils and they they tell me the same thing i just again it gets into these aren’t much fun you know what i guess they can be i
(38) 1. B: and then she grew to be you know i had pictures of her when she would sit on my hand and then she grew to be pretty big you know like a pound or something i don’t know how much and i had her for over two years and she was very a fectionate she would you know 2. A: uh-huh 3. B: crawl on me and she would sit like on my neck or my shoulder while i was working and things like that yeah much more than you would think 4. A: yeah i mean you know if you get them young and everything before they go kind of nuts so yeah rats are not my favorite animals in the world but i could see getting one from birth and everything 5. B: Yeah i sort of weird for me to have had one too but there it was a convenient little pet to have because it stayed in it’s cage and you know it was easy easy to take care of and but 6. A: yeah i- i i have friends with hamsters and gerbils and they they tell me the same thing i just again it gets into these aren’t much fun you know what i guess they can be i
(39) 1. A: yeah pretty much so but other other than that i know some also who’s claim that uh af er they have their kids or started having kids they’re going to quit their job and uh dedicate their time to raising the kids right
226 2. A: according in in putting it in their words 3. B: oh 4. A: and staying at home so i don’t know i guess each way has it’s advantages 5. B: Yeah well it’s whatever they feel comfortable with you know basically whatever they feel best and there’s probably some women who 6. B: may not do their child any good being there all the time because you know they they they can’t uh quite see themselves you know dedicated that much
(40) 1. A: yeah and there’s you know there’s people in our prisons that are not being rehabil- itated in any way shape or form and either they’re in there forever or when they get out they’re you know it’s a matter of a few months and they’re gonna be right back for the same thing so the syst- 2. B: Yeah we’re just not willing to as a society not willing to spend the time or the money to 3. B: do what it takes yeah
(41) 1. A: yeah and i think outside you can just kind of kick back and and visit af er dinner but is seems like when you’re inside the rst thing you know someone has turned on a TV and that kind of is the end of your party i think 2. B: yeah 3. B: that’s true yeah
(42) 1. A: where i work there they have uh uh recycled paper 2. A: thing so you just you have two trash cans in your o ce one one is for the regular 3. B: right 4. A: non recyclable trash and is for paper yeah and you know i think it’s great i use it all the time 5. B: Yeah you’re TI right
227 (43) 1. A: well we’re way up the other end of the state we’re uh half way between Chicago and Indianapolis on i sixty ve so and Terre Haute is you know it’s like three hours south 2. B: Yeah well i know when we ew up there we ew into Indianapolis and then had to drive down there so yeah 3. A: yeah this is a little town of ve thousand just a perfect little town for us retired people and for raising children and
(44) 1. A: well we’re way up the other end of the state we’re uh half way between Chicago and Indianapolis on i sixty ve so and Terre Haute is you know it’s like three hours south 2. B: yeah well i know when we ew up there we ew into Indianapolis and then had to drive down there so yeah 3. A: Yeah this is a little town of ve thousand just a perfect little town for us retired people and for raising children and
(45) 1. A: well tell me uh do your homes down there pretty much have garage disposals and things like that 2. B: Yeah they have everything
(46) 1. A: well i personally don’t have any problem with uh drug testing employees or po- tential employees do you 2. B: um 3. A: i mean basically 4. A: okay 5. B: basically no 6. B: um 7. A: i don’t i really can’t disagree with it 8. B: Yeah i i uh uh the policy that TI has i think is uh uh very fair
228 (47) 1. A: well i haven’t made the calls mostly i’ve just uh i answered them i live in a two story type colonial here in Maryland 2. B: Yeah that that i heard i know those words from the east when i was there but that they don’t use that word here 3. A: yeah well most the homes here are two story because uh they stack them up since the property is so expensive at least in the Maryland area where i am
(48) 1. A: well i haven’t made the calls mostly i’ve just uh i answered them i live in a two story type colonial here in Maryland 2. B: yeah that that i heard i know those words from the east when i was there but that they don’t use that word here 3. A: Yeah well most the homes here are two story because uh they stack them up since the property is so expensive at least in the Maryland area where i am
(49) 1. A: well i don’t mind electric heating and and the like it’s okay i mean i i like gas heat because it’s so nice and warm but when it comes to cooking i c- i just absolute think a gas stove’s the best that money can buy 2. B: yeah well i’ve heard peop- 3. A: you just can’t beat cooking on gas 4. B: Yeah i’ve heard people say that
(50) 1. A: uh on about a quarter of an acre or less about ten thousand square feet 2. B: Yeah the lots aren’t very big here either in the Dallas uh Fort Worth area
(51) 1. A: that’s right that’s right oh gosh well it’s it’s a real problem i’m sure 2. B: Yeah 3. B: i have friends that work in other companies that are just now adopting the policy and and stu f and i uh uh i moved to a TI facility that um had never had uh random drug sampling before
229 (52) 1. A: that isn’t that funny how we do i mean we just really 2. B: yes well you know they’re always there for you and they don’t talk back to you and uh it’s kind of pleasant yeah sure sure 3. A: Yeah well they’re companions you know they’re really well do you have any p- 4. B: yes we have a l- a dog a little white Lhasa 5. B: and her name is Angel and we have a cat who a white cat he’s he’s just a Tabby just a and his name is Dominique we call him Neek but
(53) 1. A: so the jury didn’t know about it so here they based their sentencing 2. A: thinking that this was this guy’s rst o fense and so you know we’ll give him f een which’ll teach him a lesson but it’s not just 3. B: sure 4. A: you know horrible since it was rst o fense and he’d been convicted three times before you know and and the jury was very upset and my husband came home at home very upset i mean he felt like that that they had been tricked you know and so 5. B: yeah 6. B: yeah 7. B: right 8. B: that 9. A: something i wonder if a judge didn’t have control of that if some of that would change you know 10. B: Yeah and the judge might know or at least it it wouldn’t hurt to switch over there and see if it worked any better if it didn’t we can come back for something else or or what we’ve got so well so we de nitely need a change in the system and uh
(54) 1. A: or or to give their child the leeway to choose their school not so much choose the school for them but you know kind of guide them along but let them more or less choose what they need and what they’re going to do 2. B: Yeah
230 3. B: i think it helps uh uh now my grandfather was going to pay if i went to uh what’s now UT Arlington because it was there in my hometown i could live at home but i chose to go to another school
(55) 1. A: oh it’s a uh long story but uh let’s discuss about our homes um what kind of home do you live in 2. B: uh i guess a typical home here is without a uh uh basement i guess that’s the classical uh 3. A: raised style 4. B: Yeah uh we have something here we have something here you may not have up there which is a well we we were phasing them out is the wood shingle roof i don’t know if you had it when i rst got here
(56) 1. A: now i thought that was just a sign of the times we’re all stressed out that’s what they’re trying to tell us that that we now get upset because we have to wait in line or wait in tra c but in in reality there’s so much more tra c and so many more lines and 2. A: really like you say something needs to be done about that too 3. B: Yeah it just seems to be a waste of time
(57) 1. A: i wasn’t aware of that i thought they were more or less all the same 2. B: Yeah so did i that’s that’s it was news to me too i thought it was kind of interesting though
(58) 1. A: i understand well uh you say you live in a ranch style house yourself 2. B: Yeah it’s it’s multilevel yeah like my garage i have uh uh a bedroom over
(59) 1. A: i guess you have to weigh the nancial bene t of having somebody else take care of your kids if uh the working wife can uh make uh more than enough to handle that expense i guess it’s worthwhile 2. B: Yeah it gets to be kind of expensive and i think you know some of the woman do uh weigh that um
231 (60) 1. A: i forget how she got it but it was um it was the same kind of a deal i think she got it i think she was a psych major and she got it from the psych department 2. B: Yeah mine had been a a a 3. B: how did this work a male rat had been bought and the people in the lab one day called in my friend and said you know this rat is acting really strange i think he’s sick and she said i don’t think he’s sick i think she’s pregnant
(61) 1. A: dogs are real high maintenances you got to take them for walks and you’ve got to pay a lot you know you need to pay attention to cats and everything but it’s not quite the same thing um and um 2. B: Yeah that’s true
(62) 1. A: Defense Electronic Division when i retired i know it was a you know it was a real hot hot item there i think i want to think maybe they you know really got some nudg- ing from you know D O D to do this sort of thing 2. B: yeah i used to be in DSEG myself 3. B: um A P D as a matter of fact 4. A: Yeah so you knew you know the atmosphere then
(63) 1. A: but a lot of people i think af er they thought about it for awhile and you know weighed the pros and cons decided you know it’s really not a bad idea unless you’re doing drugs of course 2. B: Yeah 3. B: i mean uh you should have no objection to it it’s like uh going down to get your license and checking your eyes i mean you know why do i need my eyes checked well we want to make sure you can see
(64) 1. A: before i retired so i was not uh ever called upon to do a drug test i certainly wouldn’t object to it and i think random is probably you know the only really fair way to do it 2. B: Yeah um i like it because um you know i i i grew up in the sixties i mean i graduated high school in sixty eight and
232 (65) 1. A: and take them down and throw it in there i mean i think that would be great i i’d support that entirely because 2. A: you know as a country we produce way too much garbage it’s it’s it’s phenomenal 3. B: Yeah way too much well i know just as an individual you know i know how many times i haul out a bag of trash and it’s like incredible if you multiply that times every- body
(66) 1. A: and my husband’s sister is the world’s worst at oh well i’ll just use the credit cards and she has all of her credit cards up to the limit 2. B: um 3. A: you know and i’m i’m thinking woman wake up and smell the roses before they come and get you but 4. B: right besides the fact that how much interest you pay and then this 5. A: right and people don’t see that either 6. B: Yeah this thing that you thought you got on sale by the time you get done paying with that with all the nance charges because you pay it o f over a long period of time you’ve spent a heck of a lot of money on that thing
(67) 1. A: and get your money back or at least exchange it for another bottle that’s full you know 2. B: that’s true 3. A: um i haven’t seen that done for a long time either 4. B: Yeah no deposit no return 5. A: yeah everything’s no deposit no return or else it’s all plastic jugs that you just pitch you know uh
B.2.2 LDC ‘yeah’ + disagree (26 examples) B.2.2.1 LDC ‘yeah’ + disagree, accept belief (15 examples)
233 (1) 1. B: o fered that our forefathers wouldn’t have even crossed their brains to have expected you know a dishwasher or a disposal or uh uh 2. B: track lighting or garage door opener you know those ki- the fancy bathrooms and stu f and yet the sturdiness of them 3. B: doesn’t a- al- doesn’t always4 4. B: measure up 5. A: Yeah i think they’re they’re trying to lower costs by cheapening in a lot of areas disagree slightly, in that it’s not a wholesale acceptance
(2) 1. B: and then she grew to be you know i had pictures of her when she would sit on my hand and then she grew to be pretty big you know like a pound or something i don’t know how much and i had her for over two years and she was very a fectionate she would you know 2. A: uh-huh 3. B: crawl on me and she would sit like on my neck or my shoulder while i was working and things like that yeah much more than you would think 4. A: yeah i mean you know if you get them young and everything before they go kind of nuts so yeah rats are not my favorite animals in the world but i could see getting one from birth and everything 5. B: Yeah i sort of weird for me to have had one too but there it was a convenient little pet to have because it stayed in it’s cage and you know it was easy easy to take care of and but 6. A: yeah i- i i have friends with hamsters and gerbils and they they tell me the same thing i just again it gets into these aren’t much fun you know what i guess they can be i
(3) 1. A: yeah well if they went AWOL what are you going to do shoot them put them in jail 2. B: Yeah i don’t know send them over to Iraq have a vacation in Iraq for a year
234 (4) 1. A: i would think you know he might i mean he might could get hurt by a car or some- thing i don’t know that he could really get killed that easily because he is so big you know but i don’t know 2. B: Yeah oh well i i hope he comes back that’s too bad how long have you had him where is the ‘yeah’??
(5) 1. B: very good uh uh the uh planning for college tuition and all the associated costs is a tough tough decision at 2. A: Yeah i think it’s it’s when you’re looking for a college that you want to go i think you really should know exactly what you want to be so you can pick the best quality
(6) 1. B: i wouldn’t have thought that Pillsbury could would the i mean or even had chem- you know well 2. A: Yeah they have chemicals that they use to re ne their ours with and stu f you know to to break the ours down and then they they also do testing there and i don’t you know wh- when you consider you know it’s probably not radioactive but toxic can be referencing the QUD - chemicals @ pillsbury - not really agreeing or disagreeing?
(7) 1. B: do you have a basement 2. A: yeah 3. B: oh yeah that’s typical up there to you know 4. A: Yeah well like i say the land here and depending on where you are and how crowded it is the land bays go up and here a little old measly quarter of an acre goes from any- where from four to ve thousand to a hundred and some odd thousand depending on where it is “yeah well
(8) 1. B: and i think well what if somebody’s dumped something back there in the and no one knows about it and you know it sinked in through the ground and all that 2. A: Yeah well i know out in some i’m not sure referring back to the QUD but not agreeing? 3. A: well i would imagine all you need is just air-conditioning
235 4. B: yeah we we have heat uh some have air-conditioning units i i i have an all electric house but uh it’s f y f y there’s a lot of gas houses here uh because it’s so popular “yeah, well we do have heat as well as air conditioning”
(9) 1. A: um they generally go abroad and and uh serve but they don’t get a salary and they give up uh X number of years of their lives to do this 2. B: Yeah but they must get some compensation somewhere
(10) 1. A: that’s uh that’s a good thought now see i hadn’t thought about it that way my rst uh thought was when they mentioned that was to people who go abroad and do service abroad 2. A: and perhaps there might be as you say more of an e fort here needed 3. B: Yeah 4. B: and we’ve done 5. B: we’ve sent 6. B: people abroad and the people that do go abroad are are usually 7. B: um 8. B: college educated talking about people abroad - doesn’t really agree than an e fort is needed but talks about college educated individuals.
(11) 1. A: so there could be a lot of things that they could put you know that chemicalwise that they use to uh that they are testing with 2. B: right you know like in 3. A: that they’re dumping 4. A: yeah 5. B: in packaging and uh you know just goodness 6. A: Yeah i don’t think they do the packaging at this plant but they do they do a lot of research and they do um they do do a lot of the re ning up there or i’m not really what’s it called when you do our i’m not yeah, I don’t think they do the packaging..
236 (12) 1. A: so i’m not sure how anybody checks up on that but um i think there’s a maximum number of kids that they can take care of at any one time 2. B: Yeah there’s been a few people that have kind of used the same person kind of like word of mouth one person in the department had them and
(13) 1. A: okay so they do go garage because i lived down south for a long time carports were real popular in certain areas where i 2. B: Yeah not Dallas uh Fort Worth is you know the two car garage and certainly all air-conditioned uh
(14) 1. A: i agree it doesn’t always work that way the way it was intended and then the people change in this country too and not and are not able to see it through our administra- tion changes also 2. B: yeah 3. A: so 4. B: Yeah i’m kind of uh a dreamer i guess i st- i still believe in 5. B: we could put this country back to work we could bring the steel mills back in we could we could build our own rails we could we could create all kinds of jobs doesn’t really agree or disagree?
(15) 1. A: but i do nd that i- in order to have your party last beyond 2. A: the dinner table there h- there does have to be something to go on to or you just kind of sit around and everybody goes to sleep and gets bored you know there’s got to be a a silly game or a 3. A: something fun to do af erwards 4. B: Yeah um-hum 5. B: i would say our dinners are probably more casual than that with um having kids and that kind of stu f it may be inviting you know neighbors over for dinner or something like that but not really we haven’t really tried a dinner party i guess
237 (16) 1. A: and supposedly we’re uh reeducating them and putting them back out in the public so give them some public service 2. B: Yeah but are we really doing that educating i- i suppose some
B.2.2.2 LDC ‘yeah’ + disagree, accept QUD (11 examples)
(1) 1. B: you know now the question is would you again go buy that same brand would you recommend that brand 2. B: to somebody or because of the experience would you just say uh don’t fool with that brand it 3. A: Yeah pr- yeah i think you’re right it’s probably hurting hurting that brand not to do that uh
(2) 1. B: wow how about that yeah that’s what it did report was a hundred and thirty relocat- ing down to McKinney 2. A: Yeah that’s possible “yeah that’s possible not agreeing or disagreeing
(3) 1. B: to do you know if you were building a house and you were going to spend you know f y thousand versus twenty thousand on materials because your 2. A: yeah 3. B: you’re going to you’re going to pay for uh technology that’s not mature yet 4. A: Yeah that that’s a problem also not accepting or denying
(4) 1. B: maybe they want to work a little bit whatever it is maybe they should think about do you really want a child or not 2. A: uh-huh 3. A: Yeah good question
238 (5) 1. B: and you know i’m actually all for that i don’t i’m probably not the b- 2. B: best person for recycling but i’ll be damned if you know if they they didn’t uh 3. B: charge me an extra ten bucks for garbage collection every month i’d be sure and re- cycle 4. A: Yeah how how how do they monitor that though i wonder not necessarily agree- ment
(6) 1. A: um i think it should all be voluntary as it is now but um i do agree that i think it is a good thing because i don’t know how else a lot of these um things could happen if they didn’t have the volunteers to do it 2. B: Yeah they uh 3. B: um the example they gave was like the Peace Corps not agreeing or disagreeing?
(7) 1. A: so i i think i’m developing a new handicraf i think it’ll become resume writing 2. B: oh yeah 3. B: oh well let’s hope that you you you aren’t e- employed by that craf for too long 4. A: uh Yeah i i hate to think about it and and i’m getting rather good at reading want ads yeah it it not agreement - just kind of accepting the QUD as a thing
(8) 1. A: Defense Electronic Division when i retired i know it was a you know it was a real hot hot item there i think i want to think maybe they you know really got some nudging from you know D O D to do this sort of thing 2. B: yeah i used to be in DSEG myself 3. B: um A P D as a matter of fact 4. A: Yeah so you knew you know the atmosphere then same as previous
(9) 1. A: well of course down there you probably don’t have land as quite as much a premium so you can sprawl a big rancher out 2. B: Yeah i guess it’s the style i really don’t have an answer here because of the tornado i always thought tornados uh i thought people would build a lot of storm cellars you
239 know or shelters you know but even that is not uh popular at all i don’t know i don’t know of anyone doing that uh
(10) 1. B: well and i guess you know you always have to think about things like your gas mileage and stu f like that you know you 2. A: oh it’s easy to get gas mileage in this car it gets excellent gas mileage 3. B: Yeah that’s one of the big throwing cards for some of the foreign ones
(11) 1. B: i i couldn’t see having sh like that i could see sort of having maybe some of the tropical sh that are really really pretty but 2. A: Yeah like i remember a friend of mine at work telling me that at his frat in college that they had a tank of piranha didn’t agree really??!
B.3 COCA ‘no’ (95 examples)
This section includes 29 examples with agreeing followup content which agreed with negation in the triggering utterance, and 59 with disagreeing content which rejected the at-issue content. The remaining seven were coded as rejecting the QUD.
B.3.1 COCA ‘no’ + agree (negative concord) (29 examples)
(1) 1. since they’re both going to be playing on the same eld. 2. CARSON DALY- (08#53:44): Unbelievable. Look at that. 3. SAVANNAH GUTHRIE- (#53:47): That looks so good. Do n’t take a bite because you have to do the birthdays. 4. AL ROKER (08:53:49): No, because I got to do the birthdays. That’s right. Wash itdown with a big old jar of Smucker’s. All right, birthday time. Wegot some great folks to celebrate with help from our friends at Smucker’s. First up, happy one hundred and f h
240 (2) 1. Well, they’re about 25 megs each. So it’s about - yeah, it’s about 800,000 images every day. 2. ROBERT SMITH#Eighthundred thousand photos a day. So no one can look through them. There’s not a human being here, watching the photos come down. 3. KATHERINE SCOTT#No. Everything is automatic. 4. ROBERT SMITH#Planet has computers that stitch together all of thephotos of the Earth every day. So you can essentially do that thing you always see in spy movies, you know, pick a spot on the Earth and then zoom in. 5. STACEY VANEK SMITH#Oh,enhance. Enhance
(3) 1. thinks it’s not – the Russia investigation is not a real thing. I mean I believe he said that repeatedly, no? 2. MARTIN#Well, he says what most of us say, which is it’s a prosecutor in search of a problem. The election was not changed – 3. COOPER#No, he says it’s a hoax, it’s fake news. I meanthat’s what he said. Anyway, we’ve got to leave it there. We’ll continue – obviously, we’re going to be talking about this for days to come. Ed Martin, thank you
(4) 1. There’s a lot of, you know, this thing is real. Weemploy a lot of people. Weare growing really rapidly. Those are, you know, real... 2. GUY RAZ#But you are not , like, swimming in money and throwing it up in the air. 3. SUSAN TYNAN#No. That is a funny thing. Westill have, you know, cash-strappedconversations. 4. GUY RAZ#Do friends of yours just assume that you’re, like, rolling in cash? 5. SUSAN TYNAN#Yeah. I laughed’ cause I started to get a lot of things from, like, all the
(5) 1. the story. 2. BOSSIE#I understand, but that’s not – I do n’t believe it’s a change. 3. TODD#Is it his voice? Was that him in the Access Hollywood tape? 4. BOSSIE#Oh, it ’s clear that’s it is.
241 5. TODD#So there is no disputing this. 6. BOSSIE#No, and I don’t think he does either. 7. TODD#Well then, Corey, explain this. You’ve been with him frankly longer than David did. Why does he have this penchant to try to change his story – a bad story. He’ll own up - when he does
(6) 1. that was - that was a tragic choice. It helped him win a few ghts, but it cost him in the long run. 2. DAVE DAVIES#Right, right. You do n’t - you do n’t build up calluses in the brain. That’s not the way the science works. 3. JONATHAN EIG#No. You know, I worked with CompuBox, which is the boxing stats company, to count how many times Ali was punched over the course of his ca- reer. And I calculate that if you include the sparring sessions and his amateur years and everything else, it’s probably about 200,000 shots
(7) 1. should be covered simply because there are other major – 2. WILLIAMS#Right. Let me just say. Another distinction to be made, in the Conyers case, the allegation – it is more of an allegation now. Youcould correct me if I’m wrong. He used taxpayer dollars – 3. KURTZ#No, that’s a fact. 4. WILLIAMS#that is o ce funds to buy o f orsilence – 5. KURTZ#Yes. 6. WILLIAMS#– this woman. 7. KURTZ#This was just a couple of years ago and it was done in secret. 8. WILLIAMS#Right. That is the case, that is di ferent than what we
(8) 1. on how you want to read into them. But he was obviously critical of the Democratic congresswoman. Some of his comments could have been interpreted as being critical
242 of the president of the United States, though he certainly did not, you know, name the president in any way. 2. MURRAY#No, he didn’t name the president. He said the president sort of delivered- his condolences in his own way. But he was very forthcoming about the fact in that sound bite you just played, that he kind of explained to the president, this is what I was told when my
(9) 1. my own vision, my own brand, but we would have access to his resources beyond just money. And that was something that was never going to happen twice. 2. GUY RAZ#And you did n’t know - at this point, you didn’t know what that was going to be. 3. WHITNEY WOLFE#No. And so it really wasn’t until a couple of weeks later when webrought on - you know, I called Chris Gulczynski and Sarah Mick, who I’d worked with that Tinder. They’re really, really talented designers, and, you know, in order to build a
(10) 1. led to make that decision. But HBO has become a place of zero tolerance. There are a lot of women in power. And we would have been angry had it gone ahead. 2. JUDY WOODRUFF#You said, I saw in one interview, that there weren’t human re- sources departments. 3. SHEILA NEVINS#No, there was no one to go to. There was no – I don’t know the word sexual harassment. I do n’t think I knew it until about 25 years ago. 4. JUDY WOODRUFF#Butthere are human resources departments now, and yet women are saying they do n’t feel safe necessarily
(11) 1. just being able to open up. And like I said, just understand that it’sokay to not be okay. Like none of us are perfect in this world. None of us are. 2. MEGYN KELLY (09:34:18): Not even Michael Phelps. 3. MICHAEL PHELPS (09:34:19): No. Hell no. But just those little things that I’ve been able tolearn and just to talk to people like you feel better. You know, like you feel better.
243 4. MEGYN KELLY (09:34:30): Yeah. 5. MICHAEL PHELPS (09:34:30): You know, like for me
(12) 1. it accurately? the question is no. the answer to that is no. The media is biased against Trump. And even if they were talking about tax cuts, they would only be saying, well, this is just a break for the richest Americans, the most wealthy Americans. 2. CAVUTO#No, no, you’re dead on. The media is not going to givehim a fair shake. And it does n’t help matters any, no matter how the president tries to structure this tax cut or to say that the rich, the very rich, certainly are going to be
(13) 1. issue is the power outages. And, you know, that’s going to be a big issue. And there will be folks who will be inconvenienced for a couple of days, which, as you know in Tampa in the middle of September, is not a pleasant thing. 2. JUDY WOODRUFF#No, it’snot. I do want to ask you about – we knowthere’ssome analysis that has been pointed out to us, analysis going back several years, experts saying that Tampa is one of the most unprepared cities in the country to deal with a major hurricane, in
(14) 1. is what I’ve been looking for – for you know, all my albums and so. It’s me. 2. MEGYN KELLY- (09#51:09): You did n’t lay down a bunch of vocals. It’s going to be us and you when you sing. 3. LEE BRICE- (09#51:11): No. Like what you hear today is what you’re going to hear on therecord. 4. MEGYN KELLY- (09#51:13): Yes, because he’s going to sing for us right af er this break. Standby because we’re going to have Lee Brice live right here next. (AN- NOUNCEMENTS) 5. MEGYN KELLY- (
244 (15) 1. in this story at all. 2. BANFIELD#So, Miss Ashley, wearing the lovely silver bracelet in this picture, appar- ently had that $34,000 deposited into her own checking account and then spent it. Listen. You ca n’t do that if you think your pal Taylor is somewhere out there. 3. JACKSON#No. The further problem there, of course, is the issue of motive,right? Why would she want her to disappear? Why would she want to exact harm upon her? Well, because I have money that I do n’t want to return. And we always talk about this
(16) 1. gay af er that. You know, he had very complicated feelings about his homosexual life. And even as he was as out there as anybody could be, he also turned on it and of en not with that , you know, that much bitterness but still would never discuss it. 2. TERRY GROSS#No. So I want to play a di ferent excerpt of ” Street Hassle, ”not the slipway part that you referred to... 3. ANTHONY DECURTIS#Sure. 4. TERRY GROSS#... But the part that’s about somebody overdosing. And the person narrating the story is, like, so cold, just so indi ferent about
(17) 1. being like, mom, he’s so cute. Like, as a twelve-year-old, like, that was awesome. 2. DENNIS MURPHY- (09#06:53): But it was n’t as that she – she was bedridden or anything at that point, so she could – 3. LYNDSAY LOVELACE- (#06:57): No. She would – 4. DENNIS MURPHY- (09#06:58): She was just feeling crummy, huh? 5. LYNDSAY LOVELACE- (#06:59): She was feeling sick. And even for my mom that was not common, because she – even if she was sick, she did what was like – like
(18) 1. alive or out of prison. I mean, at least if I can get just in the prison, then maybe some- one have to visit me or something. 2. JENNA BUSH HAGER- (#37:06): And you’re not – you’re not playing a police of- cer. 3. BROOKE SHIELDS- (10#37:08): No, I didn’t get a badge. 4. JENNA BUSH HAGER- (#37:09): Talkabout your role.
245 5. BROOKE SHIELDS- (10#37:09): I begged for a badge. I begged for one. 6. JENNA BUSH HAGER- (#37:12): Who are you? 7. BROOKE SHIELDS- (10#37:13): I ca n’t
(19) 1. ’s talk about the cost to the government. I mean, on its face, you would think, oh, well, they’re stopping the subsidies; that means the government is going to save a lot of money. But you were telling us it’s not that simple. 2. JULIE ROVNER#No. What’s ironic is that this will cost the federal government more money,according to the Congressional Budget O ce, because what happens is that insurers will raise their premiums. When they raise premiums, remember, the premium subsidy comes in. Those premium subsidies will go up to match the
(20) 1. ’s and 60’s. He loved technology. He loved the ability to communicate his message – 2. INGRAHAM#Absolutely. 3. UPDEGROVE#– to as wide as audience as possible. Reagan was alive today he would be using Facebook and Twitter. 4. INGRAHAM#Maybe it wouldn’t be just like Trump’s tweets. 5. SHIRLEY#No, it wouldn’t be. 6. INGRAHAM#It would be. He would tweet andwe would all love the tweets. Let’s face it great to have you both on. Thank you so much. I wish you were here at the library with me, though. Directly ahead, what
(21) 1. He has been in Moria for eight months, and he says the pressure of the camp caused him to attempt suicide. 2. FRANK#I was like, let me just hang myself and forget about life, instead of trying to kill somebody. 3. MALCOLM BRABANT#But you didn’t do it. 4. FRANK#No, I didn’t. 5. MALCOLM BRABANT#Did you try?
246 6. FRANK#Yes, I tried. Igot the rope. I went to the bush. At this stage, I have a thought come inside me. Are you stupid? Do you want to kill yourself? Are you crazy? What is wrong
(22) 1. GUY RAZ#So I mean, the di ference between your company and so many companies we’ve featured on the show is that you’re still in the thick of it. 2. SUSAN TYNAN#Yeah. 3. GUY RAZ#You are not, like, able to just sit back and say, let me just... 4. SUSAN TYNAN#No, we’re in the throes. 5. GUY RAZ#Yeah. 6. SUSAN TYNAN#Like, what’sgoing to happen with our major competitors? Like, are they going to try and launch stu f we’re doing? Probably. You know, will we get to be a sustainably, you know, healthy business
(23) 1. All right. First question: this is from John K. I’m going to you, Tucker. 2. CARLSON#All right. 3. GUTFELD# If you were on’ Who Wants to Be a Millionaire,’ which is a game show, if you’re not familiar with it. 4. CARLSON#No. I’m so high-toned. 5. GUTFELD#All right. Then you might not know whatI’m asking. Who would be your lifeline, Tucker? 6. CARLSON#I do n’t know what that means. 7. GUTFELD#All right. 8. CARLSON#That’s cool (ph). 9. GUTFELD#What? 10. CARLSON#Oh
247 (24) 1. Oh, I ca n’t – 2. KATHIE LEE GIFFORD (10:10:18): And to your mother’s life and your sister, your brother. Everybody. Mine. 3. HODA KOTB (10:10:22): You ca n’t really quantify. I know how much. 4. KATHIE LEE GIFFORD (10:10:24): No, you can not. It’s exponentially – it goes to the whole world. 5. HODA KOTB (10:10:26): Yes. 6. KATHIE LEE GIFFORD (10:10:28): I should n’t do that. I have to work on my arms a little bit more. 7. HODA KOTB (10:10:31):
(25) 1. I’m sure he – if he could cast a vote – and he was born in Alabama, actually – Mitch McConnell would not be voting for Roy Moore because this is – for one thing, Roy Moore is not a Mitch McConnell Republican. I mean he’s running – 2. DICKERSON#No, as David was saying. Yes. 3. PAGE#Running on a campaign to ejectMitch McConnell from the – from the lead- ership. So in no way is this – is this good for Mitch McConnell, even if it costs him one of those crucial Republican Senate seats. it’s hard to
(26) 1. he was working with at least one grand jury already, but now there’s another one, a newer one that has been set up here in Washington, which spells, according to all the experts, a really serious investigation that isn’t going to end any time soon. 2. MARK SHIELDS#No, it suggests that we’re in for some duration, that don’t plan- Thanksgiving or Christmas, that it’s going to be of long standing. I would say this, that, Judy, rst of all, a grand jury just is impaneled. Its purpose is to hear evidence
(27) 1. a lot of it was because of being hurt, because what she had been telling me was that whatever she had, as far as a relationship with Brad, she had been telling me for a week to ten days or so that that was over. 2. KEITH MORRISON#Apparently it wasn’t.
248 3. MARTY LARSON#No. And somewhere in my head I suspected that and that’swhy I sentthe e-mails. There was a lot of confusion. (Car on highway at night; exterior of apartment at night) 4. KEITH MORRISON- (vo#Confusion or something else that made him decided to drive three hours to
(28) 1. up a lot of really interesting legal issues. Like, you have an expectation of privacy in your house, but if Amazon is now installing a camera there to watch these delivery guys... 2. CAVUTO#No, it ’s pre-approved. They are not going to do this without your say-so. 3. KUNZIG#No, but now do you have expectation, the way we had problems with theEcho Dot and Amazon Echo? 4. MELCHIOR#And that’s scary. That’s de nitely all being stored up in the cloud. 5. (CROSSTALK) 6. CAVUTO#Let’ssay you live in a city or an area where things could be This depends on which “no” - either rejecting the QUD (the rst one) or accepting belief (the second one)
B.3.2 COCA ‘no’ + disagree, rejecting the at-issue content (59 examples)
(1) 1. up a lot of really interesting legal issues. Like, you have an expectation of privacy in your house, but if Amazon is now installing a camera there to watch these delivery guys... 2. CAVUTO#No, it ’s pre-approved. They are not going to do this without your say-so. 3. KUNZIG#No, but now do you have expectation, the way we had problems with theE- cho Dot and Amazon Echo? 4. MELCHIOR#And that’s scary. That’s de nitely all being stored up in the cloud. 5. (CROSSTALK) 6. CAVUTO#Let’s say you live in a city or an area where things could be This depends on which “no” - either rejecting the QUD (the rst one) or accepting belief (the second one)
249 (2) 1. up a lot of really interesting legal issues. Like, you have an expectation of privacy in your house, but if Amazon is now installing a camera there to watch these delivery guys... 2. CAVUTO#No, it ’s pre-approved. They are not going to do this without your say-so. 3. KUNZIG#No, but now do you have expectation, the way we had problems with theE- cho Dot and Amazon Echo? 4. MELCHIOR#And that’s scary. That’s de nitely all being stored up in the cloud. 5. (CROSSTALK) 6. CAVUTO#Let’s say you live in a city or an area where things could be This depends on which “no” - either rejecting the QUD (the rst one) or accepting belief (the second one)
(3) 1. video) (10:08:45): You would n’t hurt Mary? 2. KATHY CONLEY (interrogation video) (10:08:46): No. 3. ANDREA CANNING (10:08:47): I know killers come in all shapes and sizes. But Katie does not look like a killer. 4. ROBERT NELSON (10:08:52): No. But again, it – it’s poison. And she had made acomment earlier that it’s a lady’s weapon. (Police interview video of Katie Conley) 5. ANDREA CANNING (voiceover) (10:08:58): The detectives kept at it. They ques- tioned her for more than six
(4) 1. this happened or that happened because he’s an admitted liar at this point. 2. WALLACE#Do you by that? 3. WILLIAMS#No, it’s part of a deal, Congressman. that’s why it was – 4. CHAFFETZ#If they wanted to charge them with something greater, they would have. 5. WILLIAMS#No, but they didn’t want to, that’s my point. 6. CHAFFETZ#Andthey did n’t get anything. They did n’t – 7. WILLIAMS#But they would – if they – if they are making a deal, they are using it as pressure. In fact, you notice no sentencing component
250 (5) 1. 08#41:32): – at the stocking stu fer. 2. SAVANNAH GUTHRIE- (#41:33): Oh, the kids. Darn, I was thinking for adults. Okay. 3. JILL MARTIN- (08#41:36): You actually – 4. SAVANNAH GUTHRIE-(#41:36): Or the kids. 5. JILL MARTIN- (08#41:37): No. But you wear that purple tinsel in your hair. 6. SAVANNAH GUTHRIE-(#41:39): Yeah. That pink glitter in your hair. 7. JILL MARTIN- (08#41:40): There you go. Okay. So over twenty collections again on today.com. The retail, forty- ve to f y. The deal
(6) 1. we would never have been talking about him. That is what made him – 2. LEWIS#But there was a – 3. POWERS#So we live in a cultural that demeans women to the point that you, Matt, do n’t understand what’s – 4. CARDONA#Yes. 5. POWERS#– the problem here. 6. LEWIS#No, you’re being hard on Matt. 7. (CROSSTALK) 8. LEWIS#I want to make a pointabout the liberal bias. 9. COOPER#Matt, you can respond. We’ll take a quick break. Again, with two big stars Gwyneth Paltrow, Angelina Jolie speaking out, claiming Weinstein sexually harassed them. A
(7) 1. the major potential Democratic candidates for president who have come out against Obamacare and for single payer. Obamacare is being attacked on the... 2. GEORGE STEPHANOPOU#(O f-camera) They’re not against Obamacare. 3. MATT SCHLAPP#Well, but they’re trying to expand, they’re trying to... They’re try- ing to improve it. 4. MATT SCHLAPP#No, no, they’re undermining the central part premise of Oba- macare, which youall decided... Sounds good, sounds good, go ahead.
251 5. MATT SCHLAPP#You all decided to not have single payer. The lef is ghting Oba- macare. The right is ghting Obamacare. And Americans have told Republicans,
(8) 1. grieving, you know... 2. PAULA FARIS#And at the end of the day, like, why is she getting all this attention? Why is she a rock star? 3. –Because... Because she called the President out on his insensitivity. It’s that simple. It’s not that complicated. 4. PAULA FARIS#No, but the genesis, no, but the genesis of it is because asoldier was killed. 5. –Yes, and he made an inappropriate phone call two weeks too late. 6. PAULA FARIS#And that is the genesis of it, exactly, exactly, but it still feels a little insensitive. And
(9) 1. What’s all that about? And he kind of looked up at me in the hospital bed and said you can have the name when I die. So... 2. TERRY GROSS#Which is an interesting thing to say. But you didn’t change it. Like, on your book cover... 3. LOUDON WAINWRIGHT-#No. I use the third. I... 4. TERRY GROSS#... You’re Loudon Wainwright III. 5. LOUDON WAINWRIGHT-#Yeah. Sometimes when I use - when I get work as an actor, I lop o f the Roman numeral. But I’m comfortable with being the third. (Laugh- ter) My rst album was
(10) 1. wanted something along those lines. So I do n’t - I hardly ever do it, but today is special. 2. TERRY GROSS#Well, today’s autobiography day. And you do talk about the song in your book , so I thought it would be a good idea to include it. 3. LOUDON WAINWRIGHT-#No, it will be in my obituary. The ” Dead Skunk ” thing willbe there.
252 4. TERRY GROSS#It will be. It will be. So another really life-changing thing - you met the singer Kate McGarrigle. And she was a backup singer when you met her when she started writing her
(11) 1. very prestigious award in London. We saw Jeremy Irons introducing you. C 2. HARLIE ROSE#Oh, yeah. 3. GAYLE KING#It was great. Congratulations. 4. NORAH O’DONNELL#Yes, congratulation. 5. CHARLIE ROSE#Thank you. Well, I was humbled because the people there who were also being honored were much more renowned and accomplished. 6. GAYLE KING#No, they weren’t. No, they weren’t. No, they weren’t. But it was a very August group and we’re so glad you’re back. So we do not have to call you Sir Charles, I just want to be clear. 7. CHARLIE ROSE#No, no
(12) 1. to balance the budget? The stimulus money that every conservative in this country was against. A lot of it. So let’s not – let’s not give him all the credit. 2. HANNITY: With all due respect, so you expect him to not take the money. 3. HAHN: No, I expect – that’s what it’s there for. 4. HANNITY: Andthen the citizens of his state have to pay the money. He’s just getting some back. 5. PISCOPO: When are we going to draw on the resources of the United States of Amer- ica? When are we
(13) 1. the opportunity and what do we do? This oatmeal. This bland, pale oatmeal. 2. CAMPOS DUFFY#But you ca n’t – you ca n’t do... 3. FOWLER#Touchdown, America! 4. CAMPOS DUFFY#Greg, you can not do the bold reform that you and me and all of us here want. 5. FOWLER#No, not me. Count me out. I don’t want the bold reform.
253 6. CAMPOS DUFFY#OK, ne. But you can not do that without the Senate changing the rules. 7. (CROSSTALK) 8. FOWLER#That’s not true. You’ve just got to come to the table. Thank you. If
(14) 1. that we all stand together on it. 2. JUDY WOODRUFF#Well... 3. MATT SCHLAPP#Think about this. We actually ca n’t stand together even on re- placing Obamacare. That’s quite a stunning statement. 4. JUDY WOODRUFF#So, is the answer , TomDavis, to elect more Roy Moores to the Senate? 5. (CROSSTALK) 6. TOM DAVIS#No, look, the answer is – the wakeup call for Republicans is they needto work together as a team to get these passed. If not, they are going to have to work with Democrats. And that’s going to make some Republicans very unhappy. 7. JUDY WOODRUFF#But Roy Moore coming
(15) 1. T-shirt and the political junkie no-prize button in exchange for your promise of a dig- ital picture of yourself wearing same to be posted on our wall of shame. 2. TERRY#Will do. 3. NEAL CONAN#Congratulations. 4. TERRY#Thank you. 5. NEAL CONAN#And let’s go to... 6. KEN RUDIN#She didn’t sound happy. 7. NEAL CONAN#No, she’s happy. 8. KEN RUDIN#OK, you never can tell, really,on the phone. 9. NEAL CONAN#From Jersey, yeah. South Carolina, Tim Scott’s seat is up. 10. KEN RUDIN#It is, and we talked last week about Mark Sanford, the former gover- nor, who is trying
254 (16) 1. saying is it – 2. KATHIE LEE GIFFORD#35:40): Are sel es out of style now? 3. HODA KOTB- (10#35:42): And right now we should let everyone know where the wine bot is. 4. KATHIE LEE GIFFORD#35:44): You’re probably killing it. You’re killing it. 5. HODA KOTB- (10#35:45): No, actually we’re not killing it. 6. KATHIE LEE GIFFORD#35:46): Really? 7. HODA KOTB-(10#35:47): I’m winning that sel es are not out of style but not by much. Actually it does n’t seem like people care. 8. KATHIE LEE GIFFORD#35:54): People do n’t give a royal rip
(17) 1. say that for the procedure of American – 2. KING#What do you want to say to Miss Kelley, sir? 3. UNIDENTIFIED MALE#Miss Kelley, Frank Sinatra’s children are, any of them are in marriage age? 21:50:03 4. KING: They both well married and Nacy and Tina are both successful – 5. KELLEY#No, they’re not. 6. KING#What Tina is not married now, right? 7. KELLEY#No, Nancy is widowed and – 8. KING#Nancy is widow now. 9. KELLEY#– and Frank Sinatra, Jr. has never married. 10. KING#He’s never married, so all three are not married the rst
(18) 1. rewrite history. 2. LEWANDOWSKI#Chuck, you know, what’s amazing is we have n’t talked about once yet is the signi cant tax reform that the U.S. Senate just talked about. We’re talking about a story that ’s been litigated last year... 3. TODD#We’re talking about your book.
255 4. LEWANDOWSKI#No, but look – I know, but look, let’s talk about theAccess Hol- lywood tape. The American people had a month before election day when the Access Hollywood tape came out and you know what they said? We have a choice between a candidate who wants to change the direction complete rejection of what he wants to say
(19) 1. really gone wrong. So I remember that drama. 2. GUY RAZ#Did you grow up thinking entrepreneurially, like, I’m going to be an en- trepreneur because, I mean, we’re around the same age and I don’t remember that being a big part of our culture as kids. 3. SUSAN TYNAN#No, I looked at my dad and I thought I was going to be theboss. But I did n’t - that was n’t, like, I’m going to be the boss and great things come with being the boss, it was, like, and all the responsibility that comes
(20) 1. rate could be 22 percent. Well, that was not in either the Senate or the House bill. 2. WALLACE#Might I just suggest that I have saved that to ask Senator Barrasso in the next segment. 3. (CROSSTALK) 4. GRIFFIN#Preemptive. But you could lose the freedom caucus Mark Meadows - - 5. WALLACE#No, because they want to see that down to 20 percent. 6. GRIFFIN#They wantit down to 15 percent, but 20 percent was the bare minimum. So, the razor-thin margin that they pass it by in the Senate, they could lose that if they make – 7. WALLACE#Karl, we
(21) 1. pretty great e fect on the continent. I mean, they turned it into America, which to this day is a beacon of hope for the world, that’s what everyone wants to come here. So, you are trying to delegitimize that and let’s be honest about that. 2. NICHOLS#No. Again I disagree. First of all, we are talking about the mainlandof the United States. Columbus never made it here. So, we’re talking about people who came, the Europeans who came to Ellis Island and other places, that in uence we can say for the most
256 (22) 1. place to Ted Cruz who has a very strong organization on the ground in Iowa. Yes, he has a ght with FOX News and Megyn Kelly. This is sort of like the 3rd grader of Megyn Kelly . He keeps telling her, he hates her. 2. BORGER#Seventh grade. 3. KING#No, it is the third grade. He keeps telling her he hates her.And there’s some- thing else to it. But it is more than that. He knows now that Ted Cruz is going to take a lot of harpoons because Ted Cruz is going to be there. And Cruz
(23) 1. out to be a not true assumption, and so we adjusted, and I completely disagree with the take eye o f the ball. I found that to be empty political rhetoric. 2. KING: Empty political rhetoric. It was a NATO shortcoming, not a U.S. shortcoming in Afghanistan? 3. ZAKARIA: No I think that’s simply not true and I think if you look at thestatements of the time, Secretary Rumsfeld believed very, very strongly in a very light foot. He wanted to get in and get out as quickly as possible. He did n’t want to have troops on the
(24) 1. of my tour. 2. (CROSSTALK) 3. KING#OK. Well I do n’t want to believe with this, just – 4. WOODFILL-(ph)#No, you do n’t believe, I’m not telling you that I’m – 5. (CROSSTALK) 6. WOODFILL-(ph)#–I want to tell anybody else that everything that she said is wrong. 7. KING#No, give me one example. 8. WOODFILL-(ph)#The idea of me asking Frank Sinatra af erknowing him or, oh 10 years, what the ma a, was n’t ever said. The question was asked to a man they did Pochi and she knows that with that Pochi (ph), he put
257 (25) 1. of Coke is Coke reform. This is not – this is not an overhaul. It’s not reform. 2. CARLSON#You’re stealing that (ph). 3. GUTFELD#Whoever wrote sweeping reform needs their head examined. This is – this is more liberal than it is conservative. 4. FOWLER#No, I disagree. 5. GUTFELD#The top 1 percent provides 40 percent of the federalincome tax revenue. Is this helping them or hurting them? It’s hurting them. the corporate part is ne. I’m for that. The individual thing is a mess. It’s a mess.
(26) 1. meeting between the intelligence people and President-elect was a private meeting. It was a national security meeting. 2. RIVERA#You’re a news man. You know that’s not so. 3. O’REILLY#Sure, it is. There was no press conference. 4. RIVERA#Well, the substance may be frivolous – 5. O’REILLY#No, there was a leak by the U.S. Intel Community to a news organiza- tion. 6. RIVERA#Youcan not deny that the intelligence community brie ng Donald Trump on the salacious, horrifying activities – 7. O’REILLY#That is between Trump and the people in the Intel community. 8. RIVERA#Oh, absolutely not. 9. O’REILLY#No
(27) 1. means we need to, with partners to destroy them, in whatever time it takes it takes, and most people are not ready for that, but I am. 2. TODD#Are we going to need a new war authorization? You’re – it sounds like you’re arguing – 3. (CROSSTALK) 4. TODD#– No. But, by the way, you’re going to be in the minority, are n’t you? 5. GRAHAM#I do n’t know. I’m arguing that the current authorization, as long as it related to radical Islam, is enough. But here’s – the military determines who the
258 (28) 1. maybe it says he’s a Muslim. I do n’t know. Maybe he does n’t want that or he may not have one. 2. ANDERSON COOPER, -C#On Bill O’Reilly’s show, you said maybe he does n’t want to release it because it might say he’s a Muslim. 3. TRUMP#No, I didn’t say that. I said there may be something on itand they asked me like what? I said, well, perhaps because he’s a Muslim, perhaps something. Who knows what’s on it? I do n’t know. He did n’t want to release
(29) 1. know of anything like that. I do n’t know that anybody else does, either. What the o cial protocol was in that point. But does he have a point? There might be people looking at this going, well, yes, that’s a good question to ask. 2. LOUIS#No, I’d say he doesn’t have a point because what we have seenfor over a year is when ever anyone asks candidate and then president- elect, now President Donald Trump, about his actions, about what he has done, he goes to personal insults, he goes back to
(30) 1. HODA KOTB- (10#29:03): Come on, Athena. How do you feel? 2. ATHENA- (10#29:05): I feel amazing. 3. KATHIE LEE GIFFORD#29:06): How many dress sizes is that? 4. ATHENA- (10#29:08): That’s about like four dress sizes. 5. KATHIE LEE GIFFORD#29:10): No, you were four last week. So you might – 6. ATHENA- (10#29:11): Maybe a little bit more. Yeah. 7. HODA KOTB- (10#29:12): How about ve or six? 8. ATHENA- (10#29:13): Maybe ve or six. 9. HODA KOTB- (10#29:15): What
(31) 1. him win would be people like, guess what? You know, Condoleezza Rice would help him win. Marco Rubio, I do n’t know, people are going to say, he’s kind of young. Pawlenty is being underestimated and Portman. 2. GUILFOYLE#You are trying to sabotage. 3. WILLIAMS#No, I’m not, no, no.
259 4. PERINO#You don’t talk about thebig rumor this week which was General Petraeus. 5. WILLIAMS#That was a total, total rabbit. 6. PERINO#I know, I know. All right. Coming up, drama on the track today as Blade Runner
(32) 1. hear us? 2. KATHIE LEE GIFFORD (10:18:51): She just keep – 3. HODA KOTB (10:18:53): Oh, my god. She’s right there. She’s crying. 4. KATHIE LEE GIFFORD (10:18:56) : Oh, don’t cry, Susan. 5. HODA KOTB (10:18:57): No, she said she’s not crying. I’m lip reading. I’mvery happy. 6. LAUREN MINGER (10:19:02): Why is the volume not working. 7. HODA KOTB (10:19:03): We do n’t know. But we love you, mom. 8. KATHIE LEE GIFFORD (10:19:04)
(33) 1. has said during the campaign, he would go to jail if he 2. (INAUDIBLE) of what Hillary Clinton did. And that looks like it might be true. That speaks the FBI credibility. 3. KURTZ#OK. Robert Mueller did the right thing by taking this agent o f the investi- gation. 4. HEMINGWAY#No, he did not do the right thing because actually congressional in- vestigators have been askingfor months what happened there. 5. (CROSSTALK) 6. HEMINGWAY#They stonewalled and obstructed what actually happened there. They were n’t telling House investigators. 7. KURTZ#Right. 8. HEMINGWAY#And in fact, they leaked this to the media, which
260 (34) 1. had, nuclear, you know, we talk about being a peanut farmer, but nuclear engineer, not a great president. So, yes, there’s no correlation. But, look, Donald Trump sees the world through uncertain pecking order and intelligence is certainly part of that. 2. COOPER#No, he commented that the shooter in Las Vegas was probably smart. I’mnot sure that’s a great compliment. 3. LEWIS#He also complimented the leader of North Korea for being resilient and smart – 4. POWERS#And several others – 5. LEWIS#Yes, but the height thing, I want to go
(35) 1. get up every day and think I think what do I have to try to do to advance my campaign? 2. CYNTHIA MCFADDEN-#(O f-camera) Can you really let go of yesterday? 3. SENATOR HILLARY CL#Absolutely. 4. CYNTHIA MCFADDEN-#(O f-camera) So if you feel you blew it at a moment in the debate, you don’t... S 5. ENATOR HILLARY CL#No, I am so tired by the time I nally get to bed. Bythe time my head hits the pillow it’s lights out. 6. CYNTHIA MCFADDEN-#(O f-camera) I want to tell you, I’ve talked to women around the country, many who are ardent supporters of yours, some who are
(36) 1. for the stars to becoming one of them. Again, that’s on Sunday TODAY with Willie Geist. 2. SAVANNAH GUTHRIE-(#55:34): Love it. 3. CRAIG MELVIN- (08#55:35): Meanwhile, while we’re chatting , you almost set the studio on re. 4. SAVANNAH GUTHRIE-(#55:37): No, I didn’t. 5. AL ROKER (08:55:37): No, no.The burner was lef on. 6. SAVANNAH GUTHRIE-(#55:39): It was setting itself on re. 7. HODA KOTB- (08#55:41): Yes, it was. 8. SAVANNAH GUTHRIE-(#55:41): And we put out
261 (37) 1. do you think Ronald Reagan would tweet if he was alive during Twitter,do you think he would tweet. 2. SHIRLEY#Yes, he would. 3. UPDEGROVE#No, I do n’t. I disagree. 4. SHIRLEY#May I jump in here. I will make my point. Go ahead, Mark. 5. UPDEGROVE#No, I don’t think he would. Reagan was about civility. When hewent to Washington. He campaigned against a big government. Government actually got bigger during the Reagan administration. There were three platforms he campaigned on. One was ending the cold war by a clear American victory,
(38) 1. do something in North Korea because it is a sort of wag the dog or kill the messenger and they’re kind of trying to force t it. And, John, is there any evidence out there to disprove what this report, what this State Department insider is – 2. (CROSSTALK) 3. THOMAS#No, not necessarily, other than Trump’s rhetoric on the campaign trail, whichwas he does n’t think we should be involved in all these foreign entanglements. He thought the Iraq war was a mistake. So I do n’t think – you know, he said we do n’t need to
(39) 1. communication. 2. SESSIONS#I did not con rm or deny the existence of any communication between the president that I consider to be con dential. 3. UNIDENTIFIED MALE#Were you requested, been interviewed or you’ve been re- quested to be interviewed by the special counsel? 4. SESSIONS#You’ll have to ask the special counsel. 5. UNIDENTIFIED MALE#No, I’m asking you. Have you been interviewed by the special counsel inany shape or matter? 6. SESSIONS#The answer is no. No. Mr. Chairman I do n’t have to sit here and listen to his... 7. (CROSSTALK) 8. UNIDENTIFIED MALE#You’re the one who testi ed...
262 9. SESSIONS#... without having
(40) 1. come from the president himself, that it was actually written by his top personal at- torney John Dowd and Dowd is trying to mimic something that Ty Cobb, another attorney for the president, wrote on Friday. Is that customary? Do we see his attor- neys writing tweets for him of en? 2. KAREM#No. Actually, the Twitter is spread by the president himself. He gets upin the morning and tweets his, you know, to his little heart’s content, including stu f, fake video from anti-Muslim extremists. And the things he tweets of en come straight from him, come straight from
(41) 1. clear and they have to be changed. And it’s an opportunity that President Trump has created for them to x this very bad deal, which is dangerous for them, no less than it is for others . 2. DICKERSON#But they believe that changing the deal basically breaks the deal. 3. NETANYAHU#No. In fact – in fact, if you don’t change it, youbreak it. That’s what the president told them, that if they do n’t change it, if they do n’t x it, if they do n’t prevent Iran from automatically getting in a decade to a
(42) 1. certain ideas and beliefs, and we just want to be really sure that you are aware of them. 2. GAYLE KING#Julia Roberts – Julia Fiona Roberts is turning f y this year. I like it. 3. JULIA ROBERTS#Gayle. 4. GAYLE KING#I like it. 5. JULIA ROBERTS#Gosh, you’re breaking the girl code. 6. GAYLE KING#No. Look, it’s – it’s published everywhere, Julia. Listen,I’m sixty- two, and it’s great. And I wonder, do you have those kind of hang-ups that, you know, when you reach a milestone birthday? 7. JULIA ROBERTS#Well, there’s nothing to be
263 (43) 1. CARLSON#However, Democrats have made no meaningful e fort to win them over. Instead, they have made a clear and conscious decision to import new voters to replace them. And that is why that party moved has radically to the lef to the no-border position. You can’t nd... 2. FOWLER#No. We don’t... 3. CARLSON#What do you mean? I do this every– I do this every night. 4. FOWLER#Tucker, I hear you. I hear you! 5. (CROSSTALK) 6. CARLSON#You can say that all you want, but the truth is... 7. FOWLER#I hear you, but when
(44) 1. never do another thing to you. Five minutes. Do n’t ruin your friendship with me for ve minutes. 2. AMBRA GUTIERREZ#I know, but it’s kind of like, it’s too much for me. I can’t. 3. HARVEY WEINSTEIN#Please, you’re making a big scene here, please. 4. AMBRA GUTIERREZ#No, but I want to leave. 5. HARVEY WEINSTEIN#Okay, bye. Thank you. 6. ELIZABETH VARGAS#(Voiceover) Police have heard enough. They confront We- instein and bring him into the station for questioning. News of the incident quickly becomes public, but curiously lurid articles about his accuser soon begin popping up in the
(45) 1. big case with a man named Brendan Eich at Mozilla who was red because he had given money to an anti- same-sex marriage initiative and he lost his job. And Mozilla said that’s not who are we. And conservatives went insane. 2. LEWIS#They have the right to do that. 3. POWERS#No. The conservatives said that that wasn’t OK, that was an infringe- ment, he should be able to express that. So I do n’t know why he was able to express that but then these players should – 4. COOPER#We have to take a break because we’re out of time.
264 (46) 1. at Fort Hood, Texas, they had a daughter. 2. SANDY SHUPE#She’s an awesome kid. 3. SHANKAR VEDANTAM#Andwhen Jamie talks about getting grief from colleagues in the military, did you hear about that? I’m wondering if these issues of gender and sexuality came up in your conversations. 4. SANDY SHUPE#No. Jamie really didn’t, you know, discuss that kind of stu f.Yeah, Jamie would grumble about, you know, the things - the, like, you know, work issues and stu f like that. But I really do n’t remember Jamie, you know, talking about
(47) 1. And it’s the same... 2. (CROSSTALK) 3. TAMMERO#Oh, stop, Neil. 4. CAVUTO#It’s the same stupid... 5. TAMMERO#You’re hating on Star Wars now. Yes, there are a few lightsabers. 6. CAVUTO#Stop it . Really? Is there – it gets old af er a while. 7. (CROSSTALK) 8. TAMMERO#No, it never gets old for Star Wars fans. 9. CAVUTO#What is this,Episode VIII, looking backwards, or.... 10. TAMMERO#This is Episode IX, or VIII. No, Episode VIII, Yes. 11. CAVUTO#So, enough. That’s going to keep going on for decades.
(48) 1. aired out publicly and should be part of this discussion. 2. SCHIEFFER#Let me ask you this: Mayor David Bing said this morning on ABC, that no decision has yet been made on asking for a federal bailout . Do you think there is a federal bailout in Detroit’s future? 3. SNYDER#No, and I don’t expect one. I’ve said before the state cannot bail out the city of Detroit. And part of the context I would say that to you in is it’s not about just putting more money in a situation; it’s about better services to citizens
265 (49) 1. a reset button that actually made kind of sense, which we may or may not have hap- pened, I do n’t know. But that’s what this was about in my opinion. Tucker, you’re giving me that weird look you give people on your show. 2. (LAUGHTER) 3. TUCKER CARLSON, -TH#No, I’m actually agreeing with everything you say. 4. GUTFELD#You say that tothat people you’re about to yell at. You say I agree com- pletely. 5. CARLSON#But in this case it’s heart felt. 6. GUTFELD#OK. 7. CARLSON#No, I actually think that General Flynn did do something wrong
(50) 1. a larger problem is that Donald Trump is destroying norms. 2. CARDONA#Yes. 3. LEWIS#That could impact democracy and this country like ongoing. Are you con- cerned about those? 4. JENNINGS#Well, number one, you’re being a little dramatic. Number two – 5. LEWIS#I don’t think so. 6. CARDONA#No, I don’t think he is. 7. JENNINGS#– I want Donald Trump to passhis agenda because it’s the agenda that not only he ran on but virtually the entire Republican Party ran on and failure to pass repealing health care and now tax reform, people do n’t understand the political implications
(51) 1. 10#17:33): See, they – 2. JENNA BUSH HAGER- (#17:33): Okay, look. And then this one – 3. HODA KOTB- (10#17:33): Is n’t that cute? 4. JENNA BUSH HAGER- (#17:34): – likes it when I go like this. 5. HODA KOTB- (10#17:35): No, he doesn’t like it. You’re giving him a headache. No. Look, mine says no.
266 6. JENNA BUSH HAGER- (#17:39): They told me. Mine said oh. 7. HODA KOTB- (10#17:41): It responds to touch or sound and motion. So thanks to WowWe,
(52) 1. ): It did n’t entirely make sense to detectives either. (09:50:20): You let Adam go. 2. ROBERT NELSON (09:50:22): Yes. 3. ANDREA CANNING (09:50:22): But I would imagine you ’re not crossing Adam o f your list yet. 4. ROBERT NELSON (09:50:28): No. We are comfortable enough to let him go, but we didn’t sayhe absolutely had nothing to do with it. (Detectives; David King; photo of Adam Yoder) 5. ANDREA CANNING (voiceover) (09:50:33): The investigation led them to talk to Adam’s cousin and
(53) 1. ’re nished if you do the job. 2. KING#Do you want a break because of what you do, I know, is that you’re saying to New York and other places, I’ll come in and do this, please give me what you will not give someone else. 3. TRUMP#No, I’m not looking for breaks. I probably would have a harder timeget- ting breaks. I mean, you talk about Ed Koch. The man tried to ght me in so many di ferent things and he’s failed. In each case, he has failed and that’s nice.
(54) 1. . Okay. 2. KATHIE LEE GIFFORD#Okay. 3. HODA KOTB#Okay. I am moving on from Robert Duvall– 4. KATHIE LEE GIFFORD#He’s a very good dancer. 5. HODA KOTB#I’m going to make out with Eddie and I’m going to marry Birdman . What are you doing? 6. KATHIE LEE GIFFORD#I would– none of the above with– 7. HODA KOTB#No, you have to. You have to play.
267 8. KATHIE LEE GIFFORD#All right. Makeout Mary– I would make out with Eddie Redmayne because his lips are unbelievable. HODA KOTB#Next. 9. KATHIE LEE GIFFORD#I would move on from Michael Keaton, although I hear he is a lovely guy. Marry Robert Duvall,
(55) 1. . I had 73 men answering that they’ve came out, and they got red. So, this is a per- vasive problem in this country and the way we’re dealing with it by dragging it into these political environments and calling people out. It’s damaging to the victims. 2. HELDMAN#No, that’s where it exists. It exists in political environment – 3. DATIG#Weshould – 4. HELDMAN#We’re not dragged. No one is dragging it in there. 5. DATIG#We should create policies – we should create policies that deal with these problems immediately. Sexual harassment, something happens in the
(56) 1. . But now the sauce thing you were talking about. 2. SANDRA LEE-1’SEMI#You’re going to make the herb mayonnaise, are you good with that? 3. ROBIN ROBERTS-1-A#(O f-camera) I am, I’m great. 4. SANDRA LEE-1’SEMI#Okay. 5. ROBIN ROBERTS-1-A#(O f-camera)But I want to take this o f because I’m worried about... 6. SANDRA LEE-1’SEMI#No it’s good, it needs some time. 7. ROBIN ROBERTS-1-A#(O f-camera) Okay. All right. 8. SANDRA LEE-1’SEMI#You’re so cute. Okay in the, in this bowl, we have mayon- naise. 9. ROBIN ROBERTS-1-A#(O f-camera) The store-bought mayonnaise. 10. SANDRA LEE-1’SEMI#Yes, some spicy, brown mustard. We did not make the mus- tard from scratch
268 (57) 1. , you have to get the store on board, and then once the store is on board, you have to hope that the customer has the app, right? 2. GUY RAZ#Yeah. Were the stores interested when you came in? Were they like - I mean, was it... 3. WHITNEY WOLFE#No. To be honest, very few people were interested. it was re- ally howyou positioned it. And I learned a lot through that experience because, you know, one pitch would go one way, and you’d say something a little di ferent the next time, and it’d go
(58) 1. , it was a matter of normal practice and whenever we use a dynamic entry. 2. WOMAN-##Sheri f, was there anything else in that hotel room that – that made you think aside from the number of weapons that made you think he had been there for a while or – or – 3. JOSEPH LOMBARDO#No, we have information he’s been there since the 28th of September. SoI have no idea whether he prevented the housekeepers from entering the room or not, that’s just a matter of continued investigation. All right, thank you very much. We’ll provide you an update probably
(59) 1. , I’ve listened to you on the radio, and your evolution from a so-called free market capitalist to a nationalist protectionist is ba ing. 2. INGRAHAM#Really? 3. ARAMESH#It’s funny that conservatives like you keep talking about who – at least used to talk about the open market and anti-socialism – 4. INGRAHAM#No, I never did. I don’t know what you’re listening to.I never talk about the open market. 5. ARAMESH#I listened to you for ve years. 6. INGRAHAM#No, no, no, no. 7. ARAMESH#You’re a nationalist protectionist. 8. INGRAHAM#No. Arash, Arash, I
269 (60) 1. in terms of the Trumpcampaign. But remember that when you talk about Paul Man- afort or Rick Gates, who have already been indicted, these people have been charged with conspiracy, not collusion speci cally. And when we talk about – 2. WALLACE#But it had nothing to do with Russia. 3. WILLIAMS#No, conspiracy potentially that could say that there was a conspiracy to bring Russia intocooperation with the Trump campaign. 4. (CROSSTALK) 5. WALLACE#No, no, no. But, look, the Manafort charges had altered it with his - nancial dealings and it nothing to do with interference in the campaign.
B.3.3 COCA ‘no’ + agree, reject QUD (7 examples)
(1) 1. a rock, so steady that I wake up to a text message from him every single morning at the same exact time. 2. JENNA BUSH HAGER#Which, by the way, I do n’t. I’m like, does that say some- thing about me? Just kidding. It’s so sweet. 3. BARBARA BUSH#No, it just says that he’s a steady little rock, and he started because I was brokenhearted once, and he would call me every day to check on me, and then he just never stopped reaching out every day. So, that’s the sweetest. 4. JENNA BUSH HAGER#It’s the
(2) 1. – CARLSON#What should they do about it? He had background checks already. 2. CALLOWAY#The government – we can know, we can know how many – 3. (CROSSTALK) 4. CALLOWAY#Tucker, stop. This is just – this is not productive. So, let’s go to the next question. 5. CARLSON#No, but you are not answering my question. Which is what should the governmentneeds to do? 6. CALLOWAY#This is not being productive, Tucker. What we know at this point that we have allowed ourselves to end the gun control debate in this country.
270 7. CARLSON#This is just propaganda.
(3) 1. : I... MATTHEWS: Character? 2. Mr-WHITAKER: I think it’s a – well, I think it’s going to be two campaigns. I think they’re going to continue to pursue a lot of that but I think you’re not necessarily going to see it from McCain. 3. Ms-KAY: No, but you’re going to – it’s going to be there on people’s doorsteps. 4. MATTHEWS: Kathleen, character? Is that going to it, the endgame? 5. Ms-PARKER: Yeah, absolutely. And I agree with Andrew that McCain surrendered the experience argument. But that’s really all
(4) 1. Come on. 2. KATHIE LEE GIFFORD#21:10): All right. 3. HODA KOTB- (10#21:11): All right. Anyway, it’s Valentine’s Day. All right. So – 4. KATHIE LEE GIFFORD#21:15): If you want to talk about it more I’m happy to. 5. HODA KOTB- (10#21:18): No, you’re done. Fine. All right. 6. KATHIE LEE GIFFORD#21:22): Allright. There was an article in the New YorkPost. There was a piece on how everybody in life needs a work wife or I suppose they also mean work husband – 7. HODA KOTB- (10#21:30):
(5) 1. n’t commit other crimes or that necessarily that his cooperation is going to be not all that helpful. 2. SMERCONISH#So put your Holland and Knight hat on and not your hat as a former U.S. Attorney, you’dhave a eld day with someone who admitted to the felony of lying. 3. BROWNLEY#No, that’s right. that if you look at General Flynn, this issomeone who both President Obama and Attorney General Yateshad warned folks not to trust. This is an individual who was terminated from his job as National Security Advisor because he was untruthful to the Vice President and now
271 (6) 1. it’s all going to be public. And if it’s illegitimate, to which you and the president will say so. But let’s see what he nds. I would n’t exactly characterize Bob Mueller and the attorneys working for him at the federal bureau as being on a witch-hunt. 2. MARTIN#No, I’m trying to narrow the description by the media of what they’redoing. If it’s an investigation into Russia collusion, that seems narrow in focus. 3. KRISTOL#What about obstruction of justice? Is that OK with you? 4. MARTIN#It depends what it is. If it’s
(7) 1. is evil, that’s what you said in your piece. 2. YANKAH#I did n’t at all say that in piece, and if you can cite that I would be very happy to see it. What I did say – 3. CARLSON#Well, you say you can’t thrust them. 4. YANKAH#No, I did say that. 5. CARLSON#I don’t know if we are making headway. I asked you a question, can you answer it? 6. YANKAH#No. Forgive me, you ask me a question and I returned the question, so I wanted to conclude on that. 7. (LAUGHTER) 8. CARLSON
B.4 LDC ‘no’ (98 examples)
This section includes 37 examples with agreeing followup content which agreed with negation in the triggering utterance, and 33 with disagreeing content which rejected the at-issue content. The remaining 28 were coded as rejecting the QUD.
B.4.1 LDC ‘no’ + agree (negative concord) (37 examples)
272 (1) 1. B: yeah uh uh 2. A: it to whi- oh 3. B: oh i’m sorry go ahead 4. A: No that’s okay
(2) 1. B: well you are not from that area originally i can tell 2. A: No originally i’m from New Mexico i was born in New Mexico and we lived in uh Southbend for eighty eight years and uh then moved to uh Tennessee actually
(3) 1. B: and just sit and watch the game and a have a pizza and a beer that i would enjoy 2. A: uh-huh at a 3. A: at a restaurant or at home 4. B: No no at home just have it delivered yeah we we have number of our pizza places deliver and i assume that you have that there also 5. A: at home oh yeah
(4) 1. B: and my son o- my son only ve so he’s not old enough yet to send out to to the do the yard work 2. A: No but he will be
(5) 1. B: yeah so we’ve got you know we’ve got some investment in it but you really don’t get anything out of it 2. A: No you really don’t were you said you were out of state for a while was that a smaller town than than Dallas area
(6) 1. B: the big ten didn’t fair too well in the tournament 2. A: No they sure didn’t sure didn’t
(7) 1. B: of course you could annex Cuba but they wouldn’t like that a bit 2. A: No i don’t think they would
273 (8) 1. B: not too many of the situation comedies do i care for 2. A: No i don’t care for tho- 3. B: and i’m de nitely not a soap fan 4. A: too much 5. A: the only crazy comedy i really like is Saturday Night Live
(9) 1. B: like if you are land up in a such a situation where you can’t escape without taking that money then de nitely one may go for it the di ference unlike the situation where in which you are 2. B: but like practically thinking like you should not take that money it won’t be a sen- sible decision to take that money 3. A: No not at all 4. A: in fact it would be probably really harmful to you
(10) 1. B: i’m not sure that there is a solution to that as far as uh th- d- there you know every- body has in their mind the kind of day care that they want but 2. B: it seems you know that there’s not really the perfect one out there 3. A: No there’s not nobody can take care of your kids better than you
(11) 1. B: he said something about if you can win a million dollars would you not talk to your best friend anymore or something like that 2. A: oh would i would you give up your friend for money is that what it was 3. B: i think that’s the bas- what the basis of it was 4. A: ah there isn’t enough money for in the world for that 5. B: yeah and i and i totally agree i don’t think i would give up i would give up my friend- ships my best friend for that no ’cause once all the money’s gone then ah then you just have yourself your your friend will always be there for you yeah 6. A: No not for money 7. A: (( not for money ))
274 (12) 1. A: yeah there’s a lot of crazies out there that can just go in and buy a gun because they don’t really ask a lot of questions when you walk into those stores 2. B: No they don’t they don’t ask anything except how old you are i don’t think that’s kind of scary
(13) 1. A: yeah see the winter season’s just not that long to to invest a whole lot of money in in uh 2. B: and 3. A: in the the heavier heavier materials 4. B: No and you don’t um you know if you usually can wear pretty close to the same types of things just with a jacket or sweater
(14) 1. A: yeah my daughter had one i wouldn’t have one ying around my house 2. B: No i know uh
(15) 1. A: well they’re showing uh Live and Let Die at this moment and it is the rst appear- ance of Roger Moore as 007 2. B: uh-huh yeah 3. B: yeah 4. A: so it it 5. B: oh he could never cut it 6. A: No no that’s true i was just thinking that today he well in some of the later movies he looks very e feminate in the early movies whereas uh Sean Connery has maintained the
(16) 1. A: well i guess it wasn’t a situa- i mean i guess the mother wasn’t really asked to to lie anyway i guess it was you know 2. B: No she wasn’t asked to lie mhm she just was asked um what they were just say- ing you know um what how did she feel about it and she said that her daughter they were not going to put up any bail and her daughter would just have to um su fer the consequences
275 (17) 1. A: uh you had to have your car checked for pollution every year you had to have that little sticker in the window i know Texas does 2. B: uh yeah um-hum 3. A: and there are about three places in Georgia that does 4. A: and i think Louisiana does i’m not sure but i there’s not very many of them around anymore 5. B: well 6. B: uh they used to do it down in Florida now i lived down there for ten years 7. A: yeah they don’t do it anymore 8. B: No they did away with it because they found out that uh 9. A: yeah 10. B: the people that were doing it it was just a racket to them
(18) 1. A: they just grabbed but boy they sure hit pay dirt with that one ring luckily my in- surance you know reimbursed me but i can’t replace those diamonds 2. B: No and and and yeah how to put a value on a ring that you’d made
(19) 1. A: the fall is what i really miss from Pennsylvania 2. B: it just everything just kind of gives up and dies here 3. A: yeah it just turns brown and makes a mess 4. A: nothing pretty about it 5. B: No it j- the the trees just go to about the rst of December and then just oh well it’s time to die so it just it’s just an ugly mess
(20) 1. A: the companies felt they had to hire a minority 2. A: and ah im- you know i’m like you i really don’t think that that’s right 3. B: No not at all you know if someone is 4. B: quali ed no matter 5. B: what the color of their skin or where they’re from
276 (21) 1. A: one of my other daughters got a uh gerbil and they came to me for a name and i said well name him Libreg 2. A: and they said well how did you get that name i said well that’s gerbil spelled back- wards 3. B: now that’s a good idea 4. A: well they wouldn’t let me name the dog 5. B: No that one would might get you in trouble
(22) 1. A: oh they don’t last that long unfortunately yeah 2. B: No they don’t mm mm
(23) 1. A: oh i see so so there’s nobody that asks questions 2. B: so 3. B: No no there’sno one that asks questions we sit and talk about i guess the the subject of the day which is they said uh current a fairs was today
(24) 1. A: money money is not the answer 2. B: No there’s plenty of money in the system it’s just
(25) 1. A: i mean that’s all you need for like for like bullet charts and stu f you don’t need them in four colors 2. B: No uh-huh if it was a customer presentation then that would be di ferent we would want to to razzle-dazzle uh but uh
(26) 1. A: i i’m more worried about the ozone than i am anything else i i i think we’re getting a handle on sulfur dioxides and 2. A: all that kind of stu f uh cooperating with the Canadians on the acid rain problem 3. A: but the ozone i mean you know we can’t replace that 4. B: No that’s true
277 (27) 1. A: but just you know to receive a letter in the mail that says you know you need to report somewhere by next Monday you know i’m not sure that would be a terri cally good idea 2. B: No i don’t think so either because a lot of people um depending on how the public service programs are set up and i’m not -at that familiar with them to know
(28) 1. A: but i thought you had to call on three di ferent days but you don’t 2. B: No there is nothing like
(29) 1. A: and millwood i mean millwood uh they lose them and they and they still they don’t skip a beat right 2. B: No they don’t skip a beat they they have the pitching uh
(30) 1. B: yeah i think you’re right too but then i don’t i don’t really you know i don’t know i it’s a terrorist attack and you never really i guess you never know how you’ll respond to a situation until you’re put in it 2. A: No you you cannot tell until i think it’s really actually happening
(31) 1. B: but you know putting them in prison my God that doesn’t work 2. A: No apparently not because look how many years they’ve been doing that and look i mean yeah
(32) 1. A: because i gured when i got old and retire i don’t want to be working i don’t be a slave to the home 2. B: no no you don’t want to be on ladders do you
(33) 1. A: you don’t do what they say and you 2. B: no not that i promote over government or anything but you know the the world’s in a bad enough state that
278 (34) 1. B: yeah and the the ground will lter some of it but not all of it 2. A: no not when you gure i didn’t realize a cow one cow produces that much manure and they were talking like
(35) 1. A: oh so you didn’t even get the severance package 2. B: no i should have taken the the the you know uh option when that was there but it was just i missed it by a couple months
(36) 1. A: well don’t you all have mosquitoes down there 2. B: no not like not like you have up there no
(37) 1. A: yeah um well it didn’t hurt you did it to i mean you didn’t go out and charge a whole bunch and lose everything did you 2. B: no no but i have had some times when i’ve had some pretty good balances on there and uh you know i nally
B.4.2 LDC ‘no’ + disagree, rejecting the at-issue content (33 examples)
(1) 1. B: yeah and then they give give you just the local O bits and that’s it and well hell you don’t know know those people that died or or what from 2. A: No the older you get the more you know though
(2) 1. B: depending on what industry you get into or whatever like uh you know TI being electronics uh you never get out of school so you know 2. A: um-hum 3. A: No well actually i graduated 4. A: with a BS in double E in in eighty eight and i’ve been in school ever since then
279 (3) 1. B: and of course they’re not going to tell the other side of the story and they’re not going to tell it accurately 2. A: No 3. A: but i do enjoy we are here temporarily i enjoyed having CNN so that i could tune in the news any time i wanted to and not wait for network broadcasting
(4) 1. A: you would never think of having to replace the clutch in a Mercedes 2. B: no but then um 3. A: especially not af er two years 4. B: No but on the other hand i guess too uh whenever you do have to have some major work done on one of those it costs a fortune
(5) 1. A: he was really funny 2. A: both of the ones i met have been really funny guys i don’t know if that’s uh 3. A: if that’s true about all Puerto Ricans or not 4. A: ones i met have been pretty pretty funny 5. B: No i have no idea but uh so you think that mostly they’re they’re pretty much satis- ed with things the way they are
(6) 1. B: and then the spackling had to dry a day and then the painting took another day 2. A: you didn’t try we rewallpapering you just uh 3. B: No i i just painted
(7) 1. B: i’ve i’ve i’ve also thought thought ballese would be an interesting choice because it’s uh you have english speakers i mean i speak spanish but the english would help um you have the the galapagos which are close by i believe 2. A: oh yeah 3. A: oh de nitely 4. A: oh yeah
280 5. B: so there there’s a lot of beauty there too 6. A: oh i yeah actually that’s one place i really would like to go 7. B: ballese 8. A: No galapagos 9. B: yeah the galapagos it’s it’s supposed to be something else
(8) 1. B: yeah see my husband’s one of these that will switch from game to game to catch a little bit of every one you know 2. A: No i like to stick to one game
(9) 1. B: you seen the movie 2. A: No i that looked just horrible to me
(10) 1. B: yeah well i mean if if you didn’t have to 2. B: you know take the o fer ah genuinely w- would you ah would you accept it no 3. A: No i wouldn’t of course not nah money’s not worth that
(11) 1. B: yeah california is really weird about smoking 2. A: that’s you said that’s where you’re from 3. B: No no no no i’m from michigan
(12) 1. B: yeah 2. A: and that’s true um 3. A: (( and they pay me through right )) 4. B: No they send a check to you
281 (13) 1. B: would you rather go out to eat or not i think 2. B: that’s the only question i heard 3. A: No i 4. A: i think there was another one i thought there was another question
(14) 1. B: well do you think that we do it because we want it to deter crime or it’s not because we don’t want to pay for inmates to stay in prison 2. A: No i don’t think it’s a monetary thing i think we hope that it will be you know 3. B: um-hum 4. A: some sort of deterrent deterrent or you know an eye for an eye type thing or some- thing like that
(15) 1. B: well are you uh you said you’re not in a in a rich neighborhood but are there are there obviously um 2. B: drug people in the area 3. A: No not right here i i have never even seen like uh what they call a crack house or anything i wouldn’t
(16) 1. B: so well i guess i’ll let you get back to feeding your little one there 2. A: No uh he’s all done yeah yeah i set him down
(17) 1. B: oh your up in Memphis 2. A: No i used to be i’m in Texas
(18) 1. B: it was like a dead end on the other 2. B: on the other end of the phone 3. A: oh was that yesterday 4. B: yeah uh-huh 5. A: oh yesterday i wasn’t home my friend picked up
282 6. B: oh yeah 7. A: so i didn’t know what it was yeah yeah yeah 8. A: but i like i what was it do you know what the day before it was 9. B: No uh-huh i just started um 10. B: actually yesterday 11. A: yeah oh you started yesterday 12. B: yesterday is my rst day
(19) 1. B: i’m surprised that a bird uh i guess they can nd their way back but i wonder exactly where a tame bird would go 2. A: did she ever get it back 3. B: No no she never got it back i’ve heard stories like this before with parrots and all this uh because people let them out out in their house you know and y around
(20) 1. B: i was there a couple of years ago actually 2. A: oh you lived here 3. B: No i had a conference down there
(21) 1. A: you’re way too young to remember the original castle wolfenstein 2. B: No i remember that i actually have it on i have it a- actually on oppy [laughter] uh my uncle gave it to me like i don’t know i don’t know six years ago or eight years ago or something like that and i still have it somewhere in a box on a on a oppy
(22) 1. A: yeah you watching uh the sixer game 2. B: No i’m i’m i ha- i have on the channel that we get our sixer g- games on but uh i’m watching um uh enterprise
283 (23) 1. A: yeah it’s always economic and it’s economic there too 2. B: and like what the germans probably have the upper hand or the germanic 3. A: No i think the french do actually yeah the french traditionally have had all the you know manufacturing jobs and the good agricultural jobs
(24) 1. A: yeah ah yeah i ah i defended this country in a military uniform yeah yeah 2. B: really i mean you are you in the armed forces 3. A: No not now
(25) 1. A: we raise orchids 2. B: oh my 3. A: which is a little bit tough to do up here in the north but it’s a solar greenhouse we put up you know like the walls are six inches thick 4. B: yeah yeah well do you not have shade cloths and so forth and 5. B: uh-huh well 6. A: No no we we we used uh berglass uh we’re face- the the greenhouse faces solar south
(26) 1. A: once i get this last one into school then we’ll see about me going to work 2. B: right that that’s true now you you d- you d- just need to treasure all them days that you’re home with them little ones ’cause 3. B: it they it’s going so quick it’s going so quick 4. A: oh i’ll be glad when they’re gone [laughter] oh yes i will 5. B: No you won’t you won’t [laughter] you’ll look back and say oh i did i i just didn’t enjoy them enough [laughter]
284 (27) 1. A: okay so we’ve got to talk about music and um do you like classic rock or modern rock or which kind 2. B: okay 3. B: oh i guess the stu f that was done more in the seventies because that’s 4. A: No not a seventies baby
(28) 1. A: okay he did where’d he decide to go 2. B: um to Williams College in Massachusetts 3. A: uh-huh sure i’m familiar with it 4. B: and that he was he was uh trying to decide between University of Pennsylvania and Williams and it was a very di cult choice and uh 5. A: and he well he must know is he interested in law or medicine he must have a de nite profession in mind 6. B: No but that’s one of the reasons why he chose Williams that it has solid liberal arts 7. A: oh okay
(29) 1. A: oh i didn’t know how that ’cause i was waiting for them to call me i didn’t know you could call the system up 2. B: but you received the call right now 3. A: No i re- i called into the system but i i last night the system called me
(30) 1. A: oh dear well i don’t have any my big solutions all go back to elect politicians that are more sensitive because i think we can’t do it ourselves 2. B: No i i n- no i don’t think we will because you nee- you need some kind of organi- zation for it and 3. A: you need clout
(31) 1. A: now i’ve got a portable cordless is that what you’re using 2. B: No i’m using a regular phone now uh
285 (32) 1. A: i thought you were saying can you hold on oh okay 2. B: No no i was talking my little girl wanted her shoes o f
(33) 1. A: hello 2. B: hi this is frank 3. A: hi frank i’m mark how are you 4. B: okay 5. A: um where are ya 6. B: where am i 7. A: yeah 8. B: ah new jersey 9. A: new jersey oh okay you’re not too far i’m in phili 10. B: (( oh okay )) 11. A: ah so um this ah question sounds [laughter] sounds a little ludicrous if you ask me 12. B: [laughter] you’re right 13. B: ah personally speaking ah 14. B: i feel it’d be a tough thing to to accept 15. A: you would accept it 16. B: No i don’t think i i don’t think i could 17. A: oh you wouldn’t accept it no i
B.4.3 LDC ‘no’ + agree, reject QUD (28 examples)
(1) 1. B: you don’t even want to know what it cost to keep a horse you know and i 2. A: cost yeah 3. A: i think i’ll just go rent a horse if i want to ride one 4. B: right i think the best thing in the world is to rent them i think that uh 5. A: yeah i do too
286 6. B: tha- that and boats are probably the same way i’d love to have a boat too but i think uh i’m not too sure i i would get that much use out of it you know 7. A: yeah 8. A: No i mean well i do enjoy my dog uh she’s such a 9. A: oh she’s my best friend and when when i lived in Orlando she uh got paralyzed from her waist down and uh waist
(2) 1. B: you could never get use to that kind of weather 2. A: No it’s true
(3) 1. B: yeah you wouldn’t know what to say ’cause you’re just you’re mind would be 2. A: No what am i going to call my mother and i’m telling her i’m about to die
(4) 1. B: yeah in fact i think you can even uh order a magazine that keeps you up to date every day of what happened on every particular soap in case you miss it 2. A: uh-huh 3. B: but uh 4. B: that that’s not real life to me so 5. A: No i think it’d be an awful waste of time
(5) 1. B: well we place a real high priority on education in our family 2. A: No and i agree i mean my husband and i in fact i’m taking my LSATs um i’m thirty something and taking my LSATs on June tenth
(6) 1. B: well that doesn’t sound very expensive to me 2. A: No i liked it i had fun with it but uh you know uh i’m a little shaky when it comes to detail work so i couldn’t get the ne pieces so you know i had to
287 (7) 1. B: so we have to keep talking right 2. A: i don’t know i don’t know what the heck to do 3. A: i don’t think i’ve ever gotten one of these before 4. B: No 5. B: well how many times have you done this 6. A: well i mean i’ve never gotten a phone call
(8) 1. B: so but you haven’t seen any money 2. A: No i talked to someone they said something about how it takes eight weeks er which makes sense
(9) 1. B: pretty hard to go back 2. B: to pennsylvania where i was born and raised it’s got ah 3. B: it’s got fake mexican food 4. A: No fake fake mexi- like taco bell
(10) 1. B: okay well i just didn’t know that much about music and i wasn’t sure what kind of music you know 2. A: No it’s very hard because see well i mean in the whole spectrum i’d rather listen you know t- what- i listen to heavy metal or or classic rock
(11) 1. B: i don’t know how people stay at home and watch soaps and get involved in them but 2. A: No i don’t either i just don’t have any interest
(12) 1. B: but still i would hate to be on the jury that sentenced someone 2. A: so would i- i fortunately never been in that circumstance i hope i never am like like everybody else 3. B: nNoo i know when um we just we moved here um i from the 4. B: i got a summons jury summons just
288 (13) 1. A: yeah we’re not from Texas either um 2. A: we’re i grew up in Pennsylvania so 3. B: oh so you’re really 4. A: yeah it it’s not like this in Pennsylvania 5. B: No i like the winters but i don’t like the summers down here it 6. A: it’s awfully hot
(14) 1. A: well i guess our ten minutes aren’t quite up yet [laughter] 2. B: No i don’t know it’s getting close this is h- how how many of these have you done
(15) 1. A: well i guess it wasn’t a situa- i mean i guess the mother wasn’t really asked to to lie anyway i guess it was you know 2. B: No she wasn’t asked to lie mhm she just was asked um what they were just say- ing you know um what how did she feel about it and she said that her daughter they were not going to put up any bail and her daughter would just have to um su fer the consequences 3. A: (( hm )) 4. A: no i i i’m that’s interesting because i hadn’t heard any more about that story
(16) 1. A: that’s pretty interesting too but like you’re right it was over too quick [laughter] 2. B: yeah but certainly if you can try and see if you can sign up 3. A: sign sign up for what for more 4. B: No no for more see if you could get more
(17) 1. A: she is she’s the most intelligent dog i’ve ever seen 2. A: course i’m 3. A: little prejudice of course 4. B: well that’s understandable yeah that’s uh 5. A: No the rst time i brought her home she was only uh was it six weeks old
289 (18) 1. A: other than those jobs i c- i’m i’m not craf y at all so i don’t think i could come up with any kind of a craf business [laughter] 2. B: No i like cross stitch and i like latch hook and needlepoint but i don’t you know it seems like craf stores are like populated everywhere though
(19) 1. A: imagine terrorists attack when you are on the airplane here i would freak out for sure 2. B: No 3. B: oh you would freak out on an airplane
(20) 1. A: i’ve never been there my dad used to work for DuPont and they built a chemical plant down there i almost moved there when i was in high school 2. B: really 3. A: yeah 4. B: No i’ve never been there either i’ve not known but just a few people from there
(21) 1. A: i’ve never been past Ohio 2. B: you’ve never seen the great American desert 3. A: No huh-uh i’ve never seen anything out i never seen the great i’ve seen
(22) 1. A: hello 2. B: hi it’s ah roy 3. A: oh i’m ben 4. B: he 5. B: had a little bit of time to think about it kind of put you on the spot 6. B: throws you right in 7. A: that’s true you all go rst or i’ll go rst 8. B: No sure ah 9. B: i’m ah i’m thinking i’m staying
290 (23) 1. A: he really was going to was going to do surgery for f een hundred dollars or put her to sleep and i was going to have to opt to have to put her to sleep even as mu- much as i loved her i wasn’t going to spend f een hundred dollars on an experiment 2. B: oh yeah 3. B: yeah plus i’m not too sure yeah i think they are experiments too and they don’t know that much uh 4. A: No but it was just uh that spot that they put on the back of a dog uh they used to use it on cattle and uh so i started using it on her and i’d take to her to the vet once a month and get that spot put on her
(24) 1. A: but they don’t look you know like uh she had complained about it one time and the girl said she was sorry she picked up these utensils and and when back and grabbed some new ones and then put the new ones back down and the new ones looked worse than the ones that she took away so she is not even looking you know and 2. B: oh 3. B: No no and actually you know those are the kind of things that should get taken back to the management
(25) 1. A: yeah that is too much and i mean i um i can only say it’s strange your son picked those kinds of college because i spent a year at Bennington in Vermont um and that was this was twenty years ago showing my age um 2. B: oh it’s so you 3. B: uh-huh 4. B: and did you ever eat at the Blue Bin Diner 5. A: yes oh my God where are you from well let’s not talk about that 6. B: No but we’ve been there when i’m up at Williams we’ve gone up there
(26) 1. B: that’s not going to help anybody get over the problem i believe it is a a a medical problem 2. A: no ab- 3. A: that’s right
291 (27) 1. A: i don’t know i- i suppose to somebody’s way of thinking we are but um i know that when they do get out the majority of them aren’t able to nd jobs are they or get back into society because they have that stigmatism 2. B: no 3. B: that’s right and as much as you say yes you know i’ve paid my debt to society and all that it’s still there
(28) 1. B: well we place a real high priority on education in our family 2. A: no and i agree i mean my husband and i in fact i’m taking my LSATs um i’m thirty something and taking my LSATs on June tenth
292 Appendix C
Experimental data
The following table presents the number of responses for each rating and under each experimen- tal condition.
293 Table C.1: Count of acceptability ratings under experimental conditions
Stimuli Responses
PRP Follow Condi- Very Acceptable Unaccept- Very Unac- up tion acceptable able ceptable CS 77 54 25 8 CN 88 45 20 11 DS 7 10 28 119 Agreeing DN 2 9 38 115
‘Yeah’ CS 1 19 63 81 CN 15 53 60 36 DS 44 61 34 25 Disgreeing DN 49 62 37 16 CS 107 36 17 4 CN 93 50 12 9 DS 9 45 55 55 Agreeing DN 17 17 82 48
‘No’ CS 3 8 28 125 CN 17 53 50 44 DS 135 12 8 9 Disgreeing DN 49 63 32 20
“Condition” refers to the experimental conditions of convergence/divergence and scalar/nonscalar: CS = Convergent Scalar, CN = Convergent Nonscalar, DS = Divergent Scalar, DN = Divergent Nonscalar.
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