“No, They Did Not”: Dialogue Response Dynamics in Pre-trained Language Models (2022)

Abstract: A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately. In this paper we examine the extent of such dialogue response sensitivity in pre-trained language models, conducting a series of experiments with a particular focus on sensitivity to dynamics involving phenomena of at-issueness and ellipsis. We find that models show clear sensitivity to a distinctive role of embedded clauses, and a general preference for responses that target main clause content of prior utterances. However, the results indicate mixed and generally weak trends with respect to capturing the full range of dynamics involved in targeting at-issue versus not-at-issue content. Additionally, models show fundamental limitations in grasp of the dynamics governing ellipsis, and response selections show clear interference from superficial factors that outweigh the influence of principled discourse constraints.

## introduction competence in language involves understanding complex principles governing relevance of previous content and dynamics of referring back to that content. certain parts of an utterance are more central and more likely to receive a response than others, and the pragmatic and grammatical rules governing responses in dialogue interact with the nature of the content being responded to. humans are highly sensitive to these distinctions, and we can expect these sensitivities to be critical for robust models in nlp, and especially for dialogue. here we examine sensitivity to these dialogue response dynamics in pre-trained language models (plms). plms are now used as foundation for nearly every downstream nlp task, including dialogue applications (e.g., @xcite . the impressive downstream performance enabled by these models has raised important questions about what types of linguistic competence are being learned during pre-trainingand though there is a growing body of work answering aspects of this question, topics of pragmatic and dialogue competence have been relatively understudied. in this paper we focus on addressing this gap, and in particular on understanding the extent to which plms develop sensitivity to dynamics governing responses in dialogue. though these plms are not trained to engage in dialogue per se, they can be expected to encounter dialogue during training (in, for instance, novels), so it is not unreasonable to expect that they may learn about such dialogue dynamics along with other linguistic competences. the strength of these models' sensitivity to such dynamics has important implications for robustness in dialogue applications, since a strong grasp of dialogue dynamics in standard plms stands to reduce fine-tuning needs and enable more robust downstream behaviors. we begin with the notion of at-issueness. a component of an utterance is considered at-issue if it is part of the "main point" of the utterance-this is to be contrasted with side comments or mentions of background knowledge, which are not the main focus of the sentence. as we lay out in section 3.1, the distinction between at-issue and not-at-issue content of an utterance is reflected directly in the nature of responses to that utterance. we thus examine models' preferences for different responses, to assess whether the preferences reflect understanding of at-issueness and how to respond to it. we find that models show consistent preference to target at-issue (main clause) content, but mixed and overall fairly weak sensitivity when it comes to the full range of dynamics involved with at-issueness. these assessments of at-issueness sensitivity are also critically reliant on another aspect of dialogue response dynamics: ellipsis. we thus additionally make a closer examination of the extent to which constraints from context dictate models' selection of auxiliary verbs (such as did, does, would) in el-lipsis constructions. we find that although models often favor an auxiliary verb that targets the main clause, they also make frequent errors, and they very rarely favor both of the auxiliary forms that align with the prior context. these results furthermore raise the important possibility that models are highly sensitive to preferences for particular auxiliary verb types, and that this could drive the at-issueness results as well. with this in mind we revisit the at-issueness experiments, and find that, indeed, there are substantial differences in models' preferences depending on the identity of the particular verb that targets the relevant content. overall, our results suggest that plms have non-trivial gaps in their understanding of response dynamics in dialogue. our results also indicate certain differences between models: bert and roberta show strong bias toward selecting responses that target the most recent and/or main clause content, while other models show more reliance on individual auxiliary verb properties. in all cases the results indicate that these plms have not yet achieved ideal sensitivity to response dynamics involving at-issueness and ellipsis, and that effectiveness in dialogue will benefit from additional training approaches. we make all datasets and code available for further testing. foot_0 ## related work recent years have seen extensive work on analysis of plms. methodologically, some of the most popular analysis paradigms targeting model embeddings have included classification-based probing (e.g., @xcite and correlation with similarity judgments @xcite @xcite . other work has analyzed plms by eliciting and analyzing output predictions @xcite . our work here focuses primarily on the latter methodology, examining and comparing model output probabilities-however, our analysis in section 5.4 uses classification-based probing. our work also builds on approaches implementing specialized sentence generation systems that produce large annotated datasets @xcite . analyses of plms have targeted a variety of types of linguistic competence. in particular, a large body of work has studied the extent to which plms capture syntactic and semantic information @xcite @xcite @xcite . less work has addressed the extent to which plms show sensitivity to pragmatic and discourse information, as we focus on in this paper. @xcite study multilingual models in various discourse tasks via zero-shot learning. @xcite investigate lms' pragmatic competence to predict discourse connectives. @xcite report that a supervised classifier is able to identify discourse relations given syntactic features along with connectives. @xcite implement a similar idea and show that classifiers are able to predict the presence of a connective based on shallow linguistic cues. @xcite explore pre-trained language models' capability in capturing discourse level relations. we complement this existing work by branching into new areas of pragmatic and discourse knowledge, examining models' sensitivity to dialogue response dynamics. another closely related literature is that in which plms, especially transformer lms, are used for building dialogue systems directly. @xcite propose multimodal transformer networks (mtn) for visual-grounded dialogue tasks. other work investigates topic-driven language models for emotion detection in dialogues @xcite . @xcite report state-ofthe-art performance on dialogue generation using transformer-based models. there are also language models designed for and trained on dialogue or conversation, such as transfertransfo @xcite , plato @xcite , @xcite , tod-bert @xcite , dialogpt @xcite , dialogbert @xcite , and lamda @xcite . here we focus on clarifying the extent to which plms pre-trained in the standard paradigm can develop knowledge of dialogue dynamics prior to any specialized dialogue training. this line of inquiry serves to broaden our general understanding of linguistic competence of standard plms, and also has implications for use of these standard plms as foundation for further dialogue-specific training. ## ellipsis the response tests above rely on a critical prerequisite: that models understand how to use ellipsis structures like "he didn't" and "she doesn't." in the case of our items, it is specifically the case that there are only two auxiliary verbs that could possibly be appropriate in a given response sentence, because there are only two verb phrases in the context sentence that could be rejected. in this section we take a closer look at whether models' preferences for response auxiliaries reflect these broader discourse constraints on ellipsis. ## verb analysis in rejection test the previous section raises questions about the extent to which these plms have a grasp on the basic discourse constraints that govern ellipsis in response utterances-and this ellipsis serves as critical foundation for our at-issueness response tests in section 5.2. in particular, the error analysis above indicates that model probabilities are influenced in large part by biases in favor of particular frequent auxiliary verbs. in this section we thus return to our at-issueness test to examine behaviors of individual auxiliary verbs separately. figure 6 shows the percentage of the time that the models prefer targeting at-issue content, broken down by which auxiliary verb targets the arc (top row) or the main clause (bottom row). we see that for four of the models, the identity of the auxiliary verb makes a substantial difference: for instance, distilled models strongly prefer to target the main clause if "did" or "does" is the auxiliary that targets the main clause-but if "did" or "does" targets the embedded clause, these models strongly prefer targeting the embedded clause. in other words, the distilled models appear in large part simply to be biased toward preferring "did" or "does" in the response sentence. bert and roberta do not show as much verb-specific fluctuation, instead preferring to target the main clause content regardless of which auxiliary verb does so. this suggests that these two models have a relatively more robust grasp on use of ellipsis to target particular clauses in previous context. as for sensitivity to impact of headers on response dynamics, within individual verbs we occasionally see a trend such that the "wait" header results in less targeting of the main clause (xlm-roberta with "has" and "would," distilgpt with "was" and "would," etc.), but for many verbs we see no difference, or even the opposite trend. on the whole, the impact of auxiliary verb identity is for most models much stronger than that of header. ## discussion in this paper we have reported on a series of experiments testing sensitivity of pre-trained language models to dynamics involved in responding to an utterance in dialogue. we focus specifically on atissueness and ellipsis, and find that models show clear sensitivity to the special status of embedded clauses, and general preference to target main clause content-but they show mixed results in terms of understanding the interaction of response headers with targeting of at-issue versus not-atissue content. furthermore, they show certain basic limitations in their grasp of the principles governing ellipsis, with selection of auxiliaries often influenced by superficial frequency factors rather than principled discourse constraints. our findings also highlight differences between models, with certain models showing strong preference to target main clause content, and others showing stronger fluctuations based on individual auxiliary verbs. this work highlights potential for improvement in standard plms, with respect to discourse sensitivities that have real implications for language competence generally, and for dialogue in particular. the models' sensitivity to special status of embedded clauses is consistent with work indicating sensitivity to syntax in these models @xcite , and the consistency with which bert and roberta prefer auxiliaries targeting main clause content indicates that these models pick up on some interaction between ellipsis and syntax of previous context. additionally, the slight impacts of header in section 5.2 suggest that these models may pick up on the beginnings of a relationship between response types and the types of content that they target. however, the general weakness in sensitivity to headers, failure on many aspects of the ellipsis tests, and interference of superficial factors, indicate clear room for growth in capturing the full range of these discourse dynamics. from a perspective of downstream dialogue tasks, our findings indicate that discourse competence in standard plms is not sufficiently comprehensive to expect that these models can provide a fully robust foundation for dialogue applications. it is possible-though not guaranteed-that training or fine-tuning directly for dialogue could improve the robustness of models' sensitivity to the specific types of response dynamics tested for here. we leave this question for future work. 0.0 0.5 1.0 proportion bert roberta xlm-roberta d i d d o e s h a s i s w a s w o u l d 0.0 0.5 1.0 proportion distilbert d i d d o e s h a s i s w a s w o u l d distilroberta reject wait d i d d o e s h a s i s w a s w o u l d distilgpt2 figure 8: verb analysis on ellipsis test (top-2). the y-axis indicates the proportion of the verb appearing as one of top-2 predictions even when the corresponding auxiliary verb did not appear in the input sequence. proportion is calculated by header. the x-axis shows the auxiliary verb used in the target sentence. 'reject' = "no" header; 'wait' = "wait no" header.