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Predicting the Focus of Negation: Model and Error Analysis

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Predicting the Focus of Negation: Model and Error Analysis Predicting the Focus of Negation: Model and Error Analysis Md Mosharaf Hossain* Kathleen Hamilton** Alexis Palmer** Eduardo Blanco* *Department of Computer Science and Engineering, University of North Texas [email protected], [email protected] **Department of Linguistics, University of North Texas [email protected], [email protected] Abstract to the affirmative alternative in binary scenarios (e.g., from not red to green when processing The The focus of a negation is the set of tokens intended to be negated, and a key component figure could be red or green. The figure is not red). for revealing affirmative alternatives to negated In such multary scenarios, however, humans keep utterances. In this paper, we experiment with the negated representation unless the affirmative in- neural networks to predict the focus of negation. terpretation is obvious from context (e.g., humans Our main novelty is leveraging a scope detector keep not red when processing The figure is red, to introduce the scope of negation as an addi- green, yellow or blue. The figure is not red.). tional input to the network. Experimental re- From a linguistic perspective, negation is under- sults show that doing so obtains the best results stood in terms of scope and focus (Section2). The to date. Additionally, we perform a detailed error analysis providing insights into the main scope is the part of the meaning that is negated, error categories, and analyze errors depending and the focus is the part of the scope that is most on whether the model takes into account scope prominently or explicitly negated (Huddleston and and context information. Pullum, 2002). Identifying the focus is a semantic task, and it is critical for revealing implicit affir- 1 Introduction mative alternatives. Indeed, the focus of negation Negation is a complex phenomenon present in all usually contains only a few tokens, and it is rarely human languages. Horn(2010) put it beautifully grammatically modified by a negation cue such when he wrote “negation is what makes us human, as never or not. Only the focus of a negation is imbuing us with the capacity to deny, to contra- actually intended to be negated, and the resulting dict, to misrepresent, to lie, and to convey irony.” affirmative alternatives range from implicatures to Broadly speaking, negation “relates an expression e entailments as exemplified below (focus is under- to another expression with a meaning that is in lined, and affirmative alternatives are in italics): some way opposed to the meaning of e”(Horn • He didn’t report the incident to his superiors and Wansing, 2017). The key challenge to under- until confronted with the evidence. standing negation is thus to figure out the meaning He reported the incident to his superiors, but that is in some way opposed to e—a semantic and not until confronted with the evidence. highly ambiguous undertaking that comes naturally • The board didn’t learn the details about the to humans in everyday communication. millions of dollars wasted in duplicate work. Negation is generally understood to carry pos- The board learnt about the millions of dollars itive meaning, or in other words, to suggest an wasted in duplicate work, but not the details. affirmative alternative. For example, John didn’t In this paper, we experiment with neural networks leave the house implicates that John stayed inside for predicting the focus of negation. We work with the house. Hasson and Glucksberg(2006) show the largest corpus annotating the focus of negation that comprehending negation involves considering (PB-FOC, 3,544 negations), and obtain the best re- the representation of affirmative alternatives. While sults to date. The main contributions of this paper not fully understood, there is evidence that nega- are: (a) neural network architecture taking into ac- tion involves reduced access to the affirmative men- count the scope of negation and context, (b) experi- tal representation (Djokic et al., 2019). Orenes mental results showing that scope information as et al.(2014) provide evidence that humans switch predicted by an automated scope detector is more 8389 Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8389–8401 July 5 - 10, 2020. c 2020 Association for Computational Linguistics beneficial than context, (c) quantitative analysis %foci %verb with %role is focus profiling which foci are easier and harder to pre- ARG0 4.09 67.44 6.06 dict, and (d) detailed qualitative analysis providing ARG1 43.76 90.47 48.36 ARG 5.53 14.24 38.81 insights into the errors made by the models. Cru- 2 ARG 0.39 1.49 26.42 cially, the scope detector we leverage to predict 3 ARG4 0.51 0.79 64.29 focus is trained with CD-SCO, a corpus created M-NEG 26.08 99.89 26.11 independently of PB-FOC (Section2). Our results M-TMP 7.16 16.80 42.62 suggest that negation scopes may transfer across M-MNR 5.50 7.36 74.71 (a) genres (short stories vs. news) and (b) negation M-ADV 3.30 13.53 24.38 types (all negations vs. only verbal negations, i.e., M-LOC 1.01 3.72 27.27 when the negation cue modifies a verb). M-EXT 0.45 0.56 80.00 M-DIR 0.25 1.07 23.68 2 Background M-PNC 1.49 2.42 61.63 M-DIS 0.28 7.81 3.61 It is generally understood that negation has scope M-CAU 0.11 2.88 3.92 and focus. Scope is “the part of the meaning that Table 1: Analysis of PB-FOC: overall percentages of is negated” and includes all elements whose indi- foci per role, percentages of negated verbs having each vidual falsity would make the negated statement role, and percentage of each role being the focus. strictly true (Huddleston and Pullum, 2002). Con- sider the following statement (1) John doesn’t know exactly how they met. This statement is true if one of which contain a negation. CD-SCO annotates or more of the following propositions are false: all negations, including verbs (e.g., I fail to see how (1a) Somebody knows something, (1b) John is the you could have done more), adverbs (e.g., It was one who knows, (1c) exactly is the manner of know- never proved that [. ]), determiners (e.g., There ing, and (1d) how they met is what is known. Thus, is no friend like [. ]), pronouns (e.g., [. ] has the scope of the negation in statement (1) is (1a–d). yielded nothing to a careful search), affixes (e.g., The focus of a negation is “the part of the scope The inexplicable tangle seemed [. ]), and others. that is most prominently or explicitly negated”, or Other corpora annotating scope in English in- in other words, the element of the scope that is in- clude efforts with biomedical texts (Vincze et al., tended to be interpreted as false to make the overall 2008) and working with reviews (Councill et al., negative true (Huddleston and Pullum, 2002). De- 2010; Konstantinova et al., 2012). termining the focus consists in pinpointing which Corpora Annotating Focus. Although focus of parts of the scope are intended to be interpreted as negation is defined as a subset of the scope, there is true and false given the original statement. Without no corpus annotating both of them in the same texts. further context, one can conclude that the intended We work with PB-FOC, the largest publicly avail- meaning of statement (1) is John knows how they able corpus annotating focus of negation (Blanco met, but not exactly, or alternatively, that (1a–b, 1d) and Moldovan, 2011). PB-FOC annotates the focus are intended to be interpreted as true, and (1c) as of the negations marked with M-NEG role in Prop- false. This interpretation results from selecting as Bank (Palmer et al., 2005), which in turn annotates focus (1c), i.e., the manner of knowing. semantic roles on top of the Penn TreeBank (Taylor We summarize below corpora annotating scope et al., 2003). As a result, PB-FOC annotates the and focus of negation, emphasizing the ones we focus of 3,544 verbal negations (i.e., when a nega- work with. The survey by Jimenez-Zafra´ et al. tion cue such as never or not syntactically modifies (2020) provides a more comprehensive analysis a verb). As per the authors, the annotation process including corpora in languages other than English. consisted of selecting the semantic role most likely Corpus Annotating Scope. In the experiments de- to be the focus. Therefore, focus annotations in scribed here, we work with a scope detector trained PB-FOC are always all the tokens corresponding with CD-SCO (Morante and Daelemans, 2012), to a semantic role of the (negated) verb. Finally, which annotates negation cues and negation scopes M-NEG role is chosen when the focus is the verb. in two stories by Conan Doyle: The Hound of The annotations in PB-FOC were carried out taking the Baskervilles and The Adventure of Wisteria into account the previous and next sentences. We Lodge. The corpus contains 5,520 sentences, 1,227 provide examples below, and Section5 provides ad- 8390 ditional examples. We indicate the semantic roles Neural networks are hard to interpret, but there is in PropBank with square brackets, and the role evidence that they learn to process negation—to a selected as focus is underlined. certain degree—when trained to predict sentiment • Even if [that deal]ARG1 is[n’t]M-NEG analysis. Li et al.(2016) visually show that neural [revived]verb, NBC hopes to find another.
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