Attribution and Alignment: Effects of Local Context Repetition on Utterance Production and Comprehension in Dialogue (2023)

Abstract: Language models are often used as the backbone of modern dialogue systems. These models are pre-trained on large amounts of written fluent language. Repetition is typically penalised when evaluating language model generations. However, it is a key component of dialogue. Humans use local and partner specific repetitions; these are preferred by human users and lead to more successful communication in dialogue. In this study, we evaluate (a) whether language models produce human-like levels of repetition in dialogue, and (b) what are the processing mechanisms related to lexical re-use they use during comprehension. We believe that such joint analysis of model production and comprehension behaviour can inform the development of cognitively inspired dialogue generation systems.

## introduction human production in dialogue is influenced by many factors within the recent conversational history, leading speakers to repeat recently used lexical and structural elements of their own and their partners' language. these factors can involve conceptual pacts speakers make in order to establish common ground @xcite , priming of lexical or syntactic cues which influences their subsequent re-use @xcite , and other social, interpersonal, cognitive, or neural influences @xcite @xcite . language models, which are often used as the backbone of modern dialogue systems, should learn to attend to such factors in order to successfully mimic human linguistic behaviour in interaction. the pre-training data of these models typically contains fluent monologic language and little diverse dialogue data-and indeed one goal of building language generators is having them produce fluent language. a key aspect of achieving fluency is the avoidance of repetition: repetitions are typically thought of as evidence of degenerate production @xcite @xcite . recent advances in conversational language models, such as chatgpt, demonstrate neural models' impressive performance in producing humanlike, proficient language. however, despite these advances, they are yet to display human-like communicative behaviour (i.e., adhering to gricean maxims-the verbosity of such models can be high), and more nuanced, local, and partnerspecific interactions. humans in dialogue use specific communication strategies which rely on repetition, and, in particular, these are local and partnerspecific @xcite @xcite . we start from the desideratum that dialogue response generation models should also produce human-like levels of repetition. while excessive levels of repetition, designed to mimic alignment, can hinder naturalness @xcite , humans generally prefer generated dialogue that contains higher levels of alignment @xcite , which also lead to more successful communication in human-human dialogue @xcite . moreover, elements of alignment have been successfully incorporated in chat bots @xcite . investigating and understanding the mechanisms which drive more human-like patterns of repetition is critical to creating more human-like natural language generation and dialogue systems. we therefore study whether models reproduce the repetition behaviour humans display in spoken dialogue, and the extent to which this repetition is affected by contextual cues. in particular, we focus on locality effects, comparing repetition patterns of speakers with respect to their own, and their partner's language. we investigate language models' production behaviour, via measuring the extent to which they generate similar local repetitions to humans, and their comprehension behaviour, through measuring the salience they assign to a given portion of the local dialogue context when comprehending an utterance. ## experimental setup in this study, we investigate (a) to what extent repetition patterns in dialogue can be explained in terms of the re-use of lexical material in the local context; ## producing repetitions we expect human repetition patterns to be highly local, given prior results showing priming effects in the same corpora (e.g., @xcite @xcite . we also expect repetition patterns to be modulated by which dialogue partner is being repeated. in particular, we expect between-speaker repetition patterns to be the strongest given that developing shared routines can signal alignment and coordination of speakers' mental models or interpersonal synergy @xcite @xcite . we firstly analyse locality and between-vs. within-speaker repetition in human-produced utterances, then investigate whether the same patterns occur in model generations. ## interpreting model comprehension behaviour in the previous section, we investigated patterns of repetition in models' production behaviour. now we turn our attention to their comprehension behaviour, making use of interpretability techniques to analyse what properties of the utterances in the context are more salient in determining expectations for a given target utterance. we expect models to learn patterns of turn-taking from the structure and contents of the context utterances @xcite @xcite . we also expect that higher salience will be assigned to repetitions with local antecedents, in line with recency effects observed in model priming behaviour @xcite . ## discussion & conclusion repetition behaviour in dialogue, whether driven by local priming @xcite , alignment effects @xcite , conceptual pacts @xcite , or routinisation @xcite , is well attested in humans. in this study, we investigate the extent to which language models are sensitive to, and display the same local, context-specific, and shared patterns of construction repetition observed in human dialogue. we conduct an in-depth analysis using two corpora of english task-oriented and open-domain dialogue, and three autoregressive neural language models. analysing human interactions, we find that within highly local contexts (we consider dialogue samples consisting of 10 utterances), repetition effects decay with distance from antecedents, particularly when repetitions are between dialogue partners, rather than of a speaker's own language. this contrasts with and complements previous work finding no evidence of locality effects within switchboard, the same open domain corpus, when considering dialogues as a whole rather than in short excerpts @xcite , suggesting that some repeated constructions may occur in multiple short bursts @xcite over the course of a dialogue-a phenomenon that is not easily captured by more 'global' analyses. 11 full breakdown of results in appendix h.2. we then evaluate model behaviour under two lenses: production behaviour, analysed in terms of the repetition of shared constructions (i.e., word sequences re-used by both dialogue participants) in model generations, and comprehension behaviour, measured by models' attribution of salience to contextual units when processing human-produced dialogue. we find that models learn, via fine-tuning, to generate more human-like patterns of construction re-use, although the degree to which repetitions are local, context-specific, and shared varies by model. we also find that while reference-based generation quality metrics correlate with the human-likeness of the repetitions produced, corpus-level metrics like mauve fail to capture this important aspect of dialogue quality. this highlights the need for more refined corpus-level approaches to statistical evaluation which take into account local and highly contextually dependent phenomena, or at least for their integration with instance-level analyses @xcite . making use of feature attribution techniques, which provide interpretations of models' comprehension behaviour, we then explore the extent to which models are sensitive to properties of the context thought to influence human propensity to produce aligned (i.e., locally repeated and context-specific) language. we observe that when comprehending utterances, tuned models assign salience to speaker labels in a more uniform manner, and that in opendomain dialogue, models learn to assign salience over the context in a more local manner. we will follow up this study with experiments where our proposed attribution aggregation procedure is performed specifically over construction tokens in the target utterance. this may allow for more fine-grained interpretation of the relationship between repetitions and the observed local effects, as well as to investigate further psycholinguistic factors which may drive the tight coupling of local context and next utterance generation. we hope our experimental setup will inspire future work that attempts to create stronger connections between language model behaviour and findings from psycholinguistics. in particular, we look forward to seeing our attribution-based methodology being applied to other dialogue-specific phenomena, and the local, dyad-specific repetition measures we investigate applied to the development and evaluation of more adaptive and context-sensitive dialogue response generation systems.