Do dialogue representations align with perception? An empirical study (2023)

Abstract: There has been a surge of interest regarding the alignment of large-scale language models with human language comprehension behaviour. The majority of this research investigates comprehension behaviours from reading isolated, written sentences. We propose studying the perception of dialogue, focusing on an intrinsic form of language use: spoken conversations. Using the task of predicting upcoming dialogue turns, we ask whether turn plausibility scores produced by state-of-the-art language models correlate with human judgements. We find a strong correlation for some but not all models: masked language models produce stronger correlations than auto-regressive models. In doing so, we quantify human performance on the response selection task for open-domain spoken conversation. To the best of our knowledge, this is the first such quantification. We find that response selection performance can be used as a coarse proxy for the strength of correlation with human judgements, however humans and models make different response selection mistakes. The model which produces the strongest correlation also outperforms human response selection performance. Through ablation studies, we show that pre-trained language models provide a useful basis for turn representations; however, fine-grained contextualisation, inclusion of dialogue structure information, and fine-tuning towards response selection all boost response selection accuracy by over 30 absolute points.

## introduction human language processing has intrigued researchers from numerous fields for centuries @xcite . the relatively recent convergence of philosophy, psycholinguistics, and information theory has produced valuable theories of language production and comprehension by framing people as predictive processors @xcite @xcite . in particular, surprisal theory posits that comprehension effort is directly related to the predictability of a linguistic unit in its context (i.e., its surprisal) @xcite , and the smooth signal redundancy hypothesis @xcite (and related theories including uniform information density @xcite and entropy rate constancy @xcite ) demonstrates that we produce linguistic signals that tend towards uniform distributions of information, or constant predictability. such theories rely on the conditional probability of observing a linguistic unit in a particular context. traditionally, estimates of these probabilities were obtained through statistical models such as n-grams or pcfgs @xcite . by comparison, large scale language models (llms) like transformers allow much greater degrees of context to be integrated into probability estimates @xcite . llms have enabled massive progress across the continuum of nlp tasks @xcite . as such, a recent field of research has investigated the application of llms for producing estimates of human surprisal. language model quality (measured by perplexity) generally correlates with ability to predict aspects of human perception behaviour-psychometric predictive power @xcite @xcite . the vast majority of psychometric predictive power studies are based on monologue-like data and involve perception of isolated sentences. however, the most natural, innate form of language-use is communicative interaction; we learn to hold conversations with very little direct instruction where as tasks related to the production or comprehension of monological data such as reading, writing, or presenting require years of conscious effort to master. given the additional modelling capacity of current lms, there has been growing interest in using them to apply classical language processing theories to more natural forms of language like dialogue @xcite @xcite . results from these works do not align neatly with findings about perception of isolated sentences. this raises interesting questions about differences between information transmission strategies employed in monological and conversational settings, and suggests that the the alignment of language models and human perceptual behaviour for monological and isolated linguistic data may not extend to dialogue. at the same time, the field of dialogue modelling in nlp has grown substantially. much of the recent work in this field is geared towards leveraging the structural differences between monologue and dialogue data @xcite . although this has increased performance on dialogue modelling benchmark tasks such as response selection and dialogue generation, it is unclear whether increased performance on these tasks results in representations that align with human perception @xcite . to understand whether representations of dialogue align with human perception of dialogue, we ask how well language model outputs correlate with human perception of dialogue turn acceptability. to do so, we build on the novel perceptual task of rating dialogue turn plausibility proposed in our previous work @xcite . first, we study the nature of human expectations in dialogue. by recasting the rating task as a discriminative one, we find evidence that, similar to linguistic acceptability judgements, human judgements of dialogue turn plausibility are probabilistic rather than deterministic @xcite . next, we establish the psychometric predictive power of different context-dependent text representations with respect to this task by asking whether such representations are predictive of dialogue plausibility judgements. in previous work, we found a statistically significant but weak correlation between surprisal estimates from a generative dialogue-based language model and human plausibility scores for (context, response) pairs @xcite . we use this paradigm here to investigate the predictive power of other language modelling styles. in doing so, we find a strong correlation between (context, response) scores from a masked llm fine-tuned towards response-selection and human judgements. interestingly, this model also achieves "superhuman" response selection performance in the sense that it obtains a higher accuracy than participants. this finding, however, motivated our analysis into response-selection dialogue models and under what conditions they align with human perception. ## language models and human language perception multiple large-scale comparisons between the predictive power of different families of a language models provide strong evidence for a relationship between a language model's quality and its predictive power for human comprehension behaviour. behaviours include self-paced reading times and gaze duration @xcite @xcite , grammatical acceptability judgements @xcite @xcite , and brain response data @xcite . other works provide similar evidence for a close relationship between surprisal and human language perception through improved language generation under cognitive-inspired constraints @xcite . although the majority of these studies are based on a constrained definition of perception using isolated sentences, they already reflect evidence that different comprehension tasks rely on different types of linguistic expectations. @xcite finds a dissociation between the syntactic generalisation capability of lms which is heavily dependent on model architecture, and lm ability to predict human reading times. similar results were found by @xcite -bert is highly predictive of acceptability judgements but was described as "remarkably poor" for estimating reading times. these findings suggest that the alignment of language model output and comprehension behaviours is unlikely to generalise to the perception of dialogue. ## discussion using the task of upcoming dialogue turn prediction, we presented a benchmark for human performance on the widely-used task of response selection: people are able to do this task, but not perfectly. to the best of our knowledge, this is the first investigation into the perceptual validity of response selection for spoken dialogue transcripts. @xcite measure human re-sponse selection using stimuli from written dialogues grounded in a virtual world, using sets of 20 responses. however, they note that the large number of negative samples makes obtaining reliable judgements from participants difficult. the rating paradigm proposed here avoids these issues, provides information about perceptual certainty, and is independent of the choice of negative samples. human performance showed that a fundamental assumption of response selection -that a single "correct" upcoming turn can be predicted from previous context -does not reflect the reality of perception with respect to dialogue acceptability. this result supports findings in cognitive science that linguistic perception of acceptability is intrinsically probabilistic @xcite . we then used this quantification of dialogue acceptability perception to establish the psychometric predictive power of different language model families, finding that response selection performance can be used as a coarse proxy for alignment with this aspect of perception, but only to a certain extent. strong correlation of bert-fp scores and human plausibility ratings suggests that, as a training objective, the discriminative response selection task does align with some aspects of dialogue acceptability perception. however, this model achieved "superhuman" response selection performance. qualitative differences in response scoring detailed in section: error analysis also indicate that this disciminative training paradigm doesn't capture the open-ended nature of dialogue acceptability perception. on the other hand, turngpt produced a moderate correlation with plausibility judgements, but performed poorly at response selection. @xcite report similar findings regarding the response selection performance of generative models compared to retrieval-based models. we conducted preliminary analysis of quantitative and qualitative differences between autoregressive and masked lms, however given that language model architecture is known to significantly affect psychometric predictive power measured by other perceptual tasks @xcite , we believe this to be a fruitful avenue for future investigation. finally, to investigate the strong correlation between the cross-encoder-style bert-fp scores and perceptual judgements of upcoming dialogue turns, we ablated aspects of the model. in particular, we examined the importance of contextualisation in dialogue representations, the value of including turn-taking structure, and whether pre-training is beneficial. our bi-encoder models provided a small amount of dialogue structure information, however, even our best-performing bi-encoder only marginally outperforms our baseline of simply measuring the distance between sbert embeddings for response selection. this may suggest that this method of encoding dialogue structure is too weak. cross-encoding, on the other hand, provides much more granular contextualisation. we found crossencoders to be much less sensitive to the capacity of the classifier than bi-encoders. our cross-encoder outperformed the sbert baseline and bi-encoders by a large margin when dialogue structure is explicitly encoded in the input (as [eos] tokens). inclusion of dialogue structure information and finetuning towards response selection boosted response selection accuracy by over 30 absolute percentage points, highlighting the value of turn structure information for response selection models. ## conclusions in this study, we extend the concept of psychometric predictive power for language models to dialogue. we define the perceptual behaviour to be predicted in terms of expectations regarding upcoming dialogue turns, and ask whether different styles of language model are predictive of dialogue turn acceptability judgements from humans. using transcripts of spoken dialogue, we find a strong correlation between the two, but not for all models: masked language models produce higher correlations than their autoregressive counterparts. ablation studies demonstrate that features of crossencoders which enable fine-grained contextualisation are important for alignment with human acceptability judgements. framing the turn acceptability rating task as discriminative response selection, we present a benchmark for human performance on this task, one of the first to our knowledge. comparisons between human and model performance on this widely-used dialogue objective indicate that response selection can be used as a coarse proxy for alignment with perception of dialogue turn acceptability but only to a certain extent. human perception of dialogue turn acceptability is inherently probabilistic and models make different mistakes compared to humans. we hope that these findings encourage development of more perceptually-motivated evaluation and training paradigms in dialogue modelling.