He Thinks He Knows Better than the Doctors: BERT for Event Factuality Fails on Pragmatics (2021)

Abstract: We investigate how well BERT performs on predicting factuality in several existing English datasets, encompassing various linguistic constructions. Although BERT obtains a strong performance on most datasets, it does so by exploiting common surface patterns that correlate with certain factuality labels, and it fails on instances where pragmatic reasoning is necessary. Contrary to what the high performance suggests, we are still far from having a robust system for factuality prediction.

## introduction predicting event factuality foot_0 is the task of identifying to what extent an event mentioned in a sentence is presented by the author as factual. it is a complex semantic and pragmatic phenomenon: in john thinks he knows better than the doctors, we infer that john probably doesn't know better than the doctors. event factuality inference is prevalent in human communication and matters for tasks that depend on natural language understanding, such as information extraction. for instance, in the factbank example @xcite in the current state-of-the-art model for factuality prediction on english is the work of pouran ben @xcite , obtaining the best performance on four factuality datasets: factbank, meantime @xcite , uw @xcite , and uds-ih2 @xcite . traditionally, event factuality is thought to be triggered by fixed properties of lexical items. the rule-based model of @xcite took such an approach: they used lexical rules and dependency trees to determine whether an event in a sentence is factual, based on the properties of the lexical items that embed the event in question. @xcite proposed the first end-to-end model for factuality with lstms. pouran ben @xcite used bert representations with a graph convolutional network and obtained a large improvement over @xcite and over @xcite rule-based model (except for one metric on the uw dataset). however, it is not clear what these end-to-end models learn and what features are encoded in their representations. in particular, they do not seem capable of generalizing to events embedded under certain linguistic constructions. @xcite showed that the @xcite models exhibit systematic errors on megaveridicality, which contains factuality inferences purely triggered by the semantics of clause-embedding verbs in specific syntactic contexts. @xcite showed that @xcite s models fail to perform well on the commitmentbank (de @xcite , which contains events under clauseembedding verbs in an entailment-canceling environment (negation, question, modal, or antecedent of conditional). in this paper, we investigate how well bert, using a standard fine-tuning approach, foot_1 performs on seven factuality datasets, including those focusing on embedded events that have been shown to be challenging @xcite @xcite . the application of bert to datasets focusing on embedded events has been limited to the setup of natural language inference (nli) @xcite @xcite . in the nli setup, an item is a premise-hypothesis pair, with a categorical label for whether the event described in the hypothesis can be inferred by the premise. the categorical labels are obtained by discretizing the original real-valued annotations. for example, given the premise the man managed to stay on his horse (rp example in ## event factuality datasets several event factuality datasets for english have been introduced, with examples from each shown in ## linguistic approaches to factuality most work in nlp on event factuality has taken a lexicalist approach, tracing back factuality to fixed properties of lexical items. under such an approach, properties of the lexical patterns present in the sentence determine the factuality of the event, without taking into account contextual factors. we will refer to the inference calculated from lexical patterns only as expected inference. for instance, in (5), the expected inference for the event had embedded under believe is neutral. indeed, because both true and false things can be believed, one should not infer from a believes that s that s is true (in other words, believe has as o/o signature), making believe a so-called ''non-factive'' verb by opposition to ''factive'' verbs (such as know or realize, which generally entail the truth of their complements both in positive polarity sentences (1) and in entailment-canceling environments (2), @xcite ). however, lexical theories neglect the pragmatic enrichment that is pervasive in human communication and fall short in predicting the correct inference in (5), where people judged the content of the complement to be true (as indicated by the annotation score of 2.38). annabel could hardly believe that she had 2.38 a daughter about to go to university. in factbank, @xcite took a lexicalist approach, seeking to capture only the effect of lexical meaning and knowledge local to the annotated sentence: annotators were lin-uistically trained and instructed to avoid using knowledge from the world or from the surrounding context of the sentence. however, it has been shown that such annotations do not always align with judgments from linguistically naive annotators. de @xcite and @xcite re-annotated part of factbank with crowdworkers who were given minimal guidelines. they found that events embedded under report verbs (e.g., say), annotated as neutral in factbank (since, similarly to believe, one can report both true and false things), are often annotated as factual by crowdworkers. @xcite showed that their annotations also exhibit such a veridicality bias: events are often perceived as factual/ nonfactual, even when the expected inference specified by the signature is neutral. the reason behind this misalignment is commonly attributed to pragmatics: crowdworkers use various contextual features to perform pragmatic reasoning that overrides the expected inference defined by lexical semantics. there has been theoretical linguistics work arguing that factuality is indeed tied to the discourse structure and not simply lexically controlled (among others, @xcite . further, our analysis of megaveridicality shows that there is also some misalignment between the inference predicted by lexical semantics and the human annotations, even in cases without not continue to signature: +/+, expected: +, observed: -a particular person didn't continue to do -0.33 a particular thing. a particular person didn't continue to have -1.5 a particular thing. they did not continue to sit - foot_2 in silence. he did not continue to talk -3 about fish. not pretend to signature: -/-, expected: -, observed: closer to o someone didn't pretend to have -1.2 a particular thing. he did not pretend to aim -0. foot_4 at the girls. {add/warn} that signature: o/+, expected: o, observed: + someone added that a particular thing happened 2.1 . linda degutis added that interventions have 2.5 to be monitored. someone warned that a particular thing happened 2.1 . it warns that mayor giuliani 's proposed pay freeze could destroy the nypd 's new esprit de corps 2.5 . not {decline/refuse} to signature: -/o, expected: o, observed: + a particular person didn't decline to do 1.5 a particular thing. we do not decline to sanction 2.5 such a result. a particular person didn't refuse to do 2.1 a particular thing. the commission did not refuse to interpret 2.0 it. in any case, given that neural networks are function approximators, we hypothesize that bert can learn these surface-level lexical patterns in the training data. but items where pragmatic reasoning overrides the lexical patterns would probably be challenging for the model. ## model and experiment setup to analyze what bert can learn, we use the seven factuality datasets in data preprocessing the annotations of cb and rp have been collected by asking annotators to rate the factuality of the content of the complement, which may contain other polarity and modality operators, whereas in factbank annotators rated the factuality of the normalized complement, without polarity and modality operators. for example, the complement anything should be done in the short term contains the modal operator should, while the normalized complement would be anything is done in the short term. in meantime, uw, and uds-ih2, annotators rated the factuality of the event represented by a word in the original sentence, which has the effect of removing such operators. therefore, to ensure a uniform interpretation of annotations between datasets, we semiautomatically identified items in cb and rp where the complement is not normalized, 5 for which we take the whole embedded clause to be the span for factuality prediction. otherwise, we take the root of the embedded clause as the span. we also excluded 236 items in rp where the event for which annotations were gathered cannot be represented by a single span from the sentence. for example, for the post office is forbidden from ever attempting to close any office, annotators were asked to rate the factuality of the post office is forbidden from ever closing any office. simply taking the span close any office corresponds to the event of the post office close any office, but not to the event for which annotations are collected. ## quantitative analysis: expected inference here, we evaluate our hypothesis that bert can learn subtle lexical patterns, regardless of whether they align with lexical semantics theories, but struggles when pragmatic reasoning overrides the lexical patterns. to do so, we present results from a quantitative analysis using the notion of expected inference. to facilitate meaningful analysis, we generated two random train/dev/test splits of the same sizes as in ## qualitative analysis the quantitative analysis shows that the model predictions are driven by surface-level features. not surprisingly, when a gold label of an item diverges from the label of items with similar surface patterns, the model does not do well. here, we unpack which surface features are associated with labels, and examine the edge cases in which surface features diverge from the observed labels. we focus on the cb, rp, and megaveridicality datasets because they focus on embedded events well studied in the literature. ## conclusion in this paper, we showed that, although fine-tuning bert gives strong performance on several factuality datasets, it only captures statistical regularities in the data and fails to take into account pragmatic factors that play a role on event factuality. this aligns with chaves's (2020) findings for acceptability of filler-gap dependencies: neural models give the impression that they capture island constraints well when such phenomena can be predicted by surface statistical regularities, but the models do not actually capture the underlying mechanism involving various semantic and pragmatic factors. recent work has found that bert models have some capacity to perform pragmatic inferences: @xcite for scalar implicatures in naturally occurring data, @xcite for scalar implicatures and presuppositions triggered by certain lexical items in constructed data. it is, however, possible that the good performance on those data is solely driven by surface features as well. bert models still only have limited capabilities to account for the wide range of pragmatic inferences in human language.