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Testing the Rational Speech Act Model of Scope Ambiguity Proceedings of the Society for Computation in Linguistics Volume 4 Article 24 2021 Every ambiguity isn’t syntactic in nature: Testing the Rational Speech Act model of scope ambiguity Sherry Yong Chen Massachusetts Institute of Technology, [email protected] Bob van Tiel Donders Institute for Brain, Cognition and Behaviour, [email protected] Follow this and additional works at: https://scholarworks.umass.edu/scil Part of the Computational Linguistics Commons, Psycholinguistics and Neurolinguistics Commons, and the Semantics and Pragmatics Commons Recommended Citation Chen, Sherry Yong and van Tiel, Bob (2021) "Every ambiguity isn’t syntactic in nature: Testing the Rational Speech Act model of scope ambiguity," Proceedings of the Society for Computation in Linguistics: Vol. 4 , Article 24. DOI: https://doi.org/10.7275/h3rp-m711 Available at: https://scholarworks.umass.edu/scil/vol4/iss1/24 This Paper is brought to you for free and open access by ScholarWorks@UMass Amherst. It has been accepted for inclusion in Proceedings of the Society for Computation in Linguistics by an authorized editor of ScholarWorks@UMass Amherst. For more information, please contact [email protected]. Every ambiguity isn’t syntactic in nature: Testing the Rational Speech Act model of scope ambiguity∗ Sherry Yong Chen Bob van Tiel Department of Linguistics & Philosophy Donders Institute for Massachusetts Institute of Technology Brain, Cognition and Behaviour [email protected] [email protected] Abstract parses. The surface scope parse corresponds to Utterances like ‘Every guest didn’t leave’ are the surface ordering of the words in the utterance, ambiguous between a reading according to and results in the ‘none’ reading given in (1a) (e.g., which no guest left and a reading according Carden, 1970). For the inverse scope parse, the to which not all of the guests left. This am- negation takes scope over the universal quantifier biguity is often explained by assuming that rather than from its surface position, thus resulting ‘every-not’ utterances have two different syn- in the ‘not-all’ reading given in (1b). tactic parses. However, experimental studies have shown that pragmatic factors, such as It has been observed that the two readings of prior probabilities and the question under dis- (1) are not always equally accessible (e.g., Horn, cussion, also play an important role in the inter- 1989, p. 226–231). For example, Musolino(1998) pretation of ambiguous ‘every-not’ utterances. reported that upon hearing the utterance in (1) with- Recently, Scontras and Pearl(2020) put for- out any context, 5-year old children generally ar- ward a probabilistic model of ambiguity reso- rived at the ‘none’ reading, whereas adults over- lution that makes it possible to quantify the rel- whelmingly favored the ‘not-all’ reading. Initially, ative contribution of syntactic and pragmatic this difference was thought to show that children factors. Here, we present three experiments aimed at testing this model and measuring the only generate a subset of the syntactic parses that division of labor between syntax and pragmat- adults do; in particular, it was thought that children ics. Our results suggest that variability in the are unable to generate (or maintain in memory) interpretation of ‘every-not’ utterances can be a parse that involves moving constituents out of explained almost entirely in terms of pragmat- their surface positions. (Recall that the ‘not-all’ ics, suggesting only a marginal role for syntax. reading is assumed to involve the negation taking 1 Introduction scope above the quantifier, contrary to the linear ordering.) Utterances containing ‘every’ and ‘not’ may be However, further evidence has shown that the ambiguous between two different readings: accessibility of the two readings is modulated by (1) Every horse didn’t jump over the fence. pragmatic factors—both for children and adults. a. None of the horses jumped over the fence. One such factor is the question under discussion (‘none’ reading) (QUD) (e.g., Conroy et al., 2008; Gualmini et al., b. Not all horses jumped over the fence. 2008). The idea underlying the notion of QUD is (‘not-all’ reading) that discourse transpires with respect to an implicit or explicit question. Any utterance in the discourse Under the ‘none’ reading, none of the horses should address this question in order to be pragmat- jumped over the fence. Under the ‘not-all’ reading, ically felicitous (Roberts, 2012). This constraint it is not the case that every horse jumped over the is also known as the question-answer requirement fence, though some may have. (Gualmini et al., 2008). This ambiguity can be explained by assuming To illustrate the potential effect of the question- that utterances like (1) have two possible syntactic answer requirement on the interpretation of utter- 0All materials and data can be found in the GitHub ances like (1), consider the following two QUDs: repository of this project: https://github.com/linguistsherry/ qud-rsa-scil2021 (2) a. Did any of the horses jump over the fence? 254 Proceedings of the Society for Computation in Linguistics (SCiL) 2021, pages 254-263. Held on-line February 14-19, 2021 b. Did all of the horses jump over the fence? Pearl’s (2020) computational model of ambiguity resolution which uses both syntactic and pragmatic The ‘every-not’ utterance in (1) only addresses the factors as independent ingredients. In the next sec- ‘any?’-QUD in (2a) if it receives its ‘none’ reading. tion, we give a brief description of the model. Af- After all, on its ‘not-all’ reading, it could be that ei- terwards, we test the model on the basis of three ther none or some but not all of the horses jumped experiments in which we measured the salience over the fence, so that the question is still unre- of different QUDs (Exp. 1), listeners’ prior expec- solved. By contrast, in the case of the ‘all?’-QUD tations about the state of the world (Exp. 2), and in (2b), the ‘none’ reading provides an overinfor- their interpretation of ambiguous ‘every-not’ ut- mative answer to the QUD, i.e., it conveys more terances such as (1) (Exp. 3). Our results suggest information than a simple ‘no’. Hence, it may be that the variability in the interpretation of ‘every- hypothesized that the ‘all?’-QUD makes the ‘not- not’ utterances can be explained almost entirely on all’ reading more salient, whereas the ‘any?’-QUD the basis of pragmatic factors, without assuming a makes the ‘none’ reading more salient. substantial explanatory role for syntax. In line with this hypothesis, Gualmini and col- The rest of the paper is organized as follows: in leagues (2008) found that 5-year old children were Section2 we introduce Scontras and Pearl’s ambi- able to access the ‘not-all’ reading when the ‘all?’- guity resolution model; Section3 provides details QUD was made sufficiently salient. This finding of the experiments which measured human perfor- suggests that the aforementioned difference be- mance; Section4 discusses results from computa- tween children and adults in the interpretation of tional modelling; Section5 concludes. ‘every-not’ utterances is more likely to have a prag- matic than syntactic source. For example, it could 2 Computational model be that children standardly assume an ‘any?’-QUD while adults are able to consider different QUDs To model our data, we make use of the ambiguity and choose one that makes the utterance true on its resolution model formulated by Scontras and Pearl QUD-appropriate reading. (2020). Here, we provide a concise description Another factor that has been argued to influence of the model. The interested reader is referred the interpretation of ‘every-not’ utterances is lis- to Scontras and Pearl(2020) for a more detailed teners’ prior expectations (Gualmini, 2004). For exposition (cf. also Savinelli et al., 2017, 2018). example, it has been found that participants are The ambiguity resolution model proposed by more likely to arrive at a ‘not-all’ reading of the Scontras and Pearl is an extension of the more ambiguous utterance in (1) if it is a priori likely general Rational Speech Acts (RSA) model (e.g., that an ‘all’ situation holds, i.e., if it is likely that Frank and Goodman, 2012). The starting point of all of horses jumped over the fence. This finding the RSA model is the stipulation of a set of possible can be explained by assuming that the probabil- messages M and a set of possible states S. In the ity of the ‘all’ situation makes the utterance of the case at hand, Scontras and Pearl assume there are ‘every-not’ utterance contextually felicitous (Wa- just two possible messages: the ambiguous ‘every- son, 1972). An alternative explanation more in not’ utterance and a null message, i.e.: line with our framework is that listeners attempt to have their interpretations differ minimally from M = m , m { every-not null} their prior expectations (Degen et al., 2015). In summary, the traditional idea is that the two Conceptually, the null message can be seen as repre- readings of ‘every-not’ utterances are due to a syn- senting messages that speakers may produce when- tactic ambiguity. However, it has since become ever the ambiguous ‘every-not’ utterance is unsat- clear that the accessibility of the readings is also isfactory. heavily dependent on pragmatic factors, such as Scontras and Pearl assume there are three possi- the QUD and prior expectations. It is still an open ble states, depending on the number of individuals question how much syntactic and pragmatic factors that satisfy the predicate (e.g., the number of horses impact the interpretation of ‘every-not’ utterances. that jumped over the fence). For convenience, we Thus, in this paper, we investigate the division of deviate notationally from Scontras and Pearl, and labor between syntax and pragmatics.
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