We model utterance production as probabilistic cost-sensitive choice over contextual alternatives, using information-theoretic notions of cost.We distinguish between goal-directed alternatives that realise a fixed communicative intent and goal-agnostic alternatives defined only by contextual plausibility, allowing us to derive speaker-and listener-oriented interpretations of different cost measures.We present a procedure to generate both types of alternative sets using language models.Analysing production choices in open-ended dialogue under both deterministic and probabilistic cost minimisation, we find that surprisal minimisation relative to goal-directed alternatives provides the strongest predictive account under both analyses.By contrast, uniform information density and length-based costs exhibit weaker and less consistent predictive power across conditions.More broadly, our study suggests that alternative-conditioned optimisation with LMgenerated alternatives provides a principled framework for studying speaker and listener pressures in naturalistic language production. 1

We investigate the roles that efficiency and effectiveness play in speakers’ repetition of shared word sequences, or constructions, in task-oriented dialogue. We find that repeating constructions has negative effects on information rate and positive effects on rate of delivery, that information rate managing strategies are predictive of task success, and that this varies by the communicative function of the constructions being repeated. More effective dialogue is characterised by greater levels of shared construction usage and more efficient task-related repetition; while task-agnostic repetition can seem redundant, it can serve important efficiency and effectiveness functions. Our results provide a nuanced picture of the importance of repetition and of developing a shared lexicon for both efficiency and effectiveness in task-oriented dialogue.

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 humanlike 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.

Abstract We investigate the extent to which modern neural language models are susceptible to structural priming, the phenomenon whereby the structure of a sentence makes the same structure more probable in a follow-up sentence. We explore how priming can be used to study the potential of these models to learn abstract structural information, which is a prerequisite for good performance on tasks that require natural language understanding skills. We introduce a novel metric and release Prime-LM, a large corpus where we control for various linguistic factors that interact with priming strength. We find that Transformer models indeed show evidence of structural priming, but also that the generalizations they learned are to some extent modulated by semantic information. Our experiments also show that the representations acquired by the models may not only encode abstract sequential structure but involve certain level of hierarchical syntactic information. More generally, our study shows that the priming paradigm is a useful, additional tool for gaining insights into the capacities of language models and opens the door to future priming-based investigations that probe the model’s internal states.1

The Uniform Information Density principle states that speakers plan their utterances to reduce fluctuations in the density of the information transmitted.In this paper, we test whether, and within which contextual units this principle holds in task-oriented dialogues.We show that there is evidence supporting the principle in written dialogues where participants play a cooperative reference game as well as in spoken dialogues involving instruction giving and following.Our study underlines the importance of identifying the relevant contextual components, showing that information content increases particularly within topically and referentially related contextual units.

Dialogue participants often refer to entities or situations repeatedly within a conversation, which contributes to its cohesiveness.Subsequent references exploit the common ground accumulated by the interlocutors and hence have several interesting properties, namely, they tend to be shorter and reuse expressions that were effective in previous mentions.In this paper, we tackle the generation of first and subsequent references in visually grounded dialogue.We propose a generation model that produces referring utterances grounded in both the visual and the conversational context.To assess the referring effectiveness of its output, we also implement a reference resolution system.Our experiments and analyses show that the model produces better, more effective referring utterances than a model not grounded in the dialogue context, and generates subsequent references that exhibit linguistic patterns akin to humans.

The role of alignment between interlocutors in second language learning is different to that in fluent conversational dialogue.Learners gain linguistic skill through increased alignment, yet the extent to which they can align will be constrained by their ability.Tutors may use alignment to teach and encourage the student, yet still must push the student and correct their errors, decreasing alignment.To understand how learner ability interacts with alignment, we measure the influence of ability on lexical priming, an indicator of alignment.We find that lexical priming in learner-tutor dialogues differs from that in conversational and task-based dialogues, and we find evidence that alignment increases with ability and with word complexity.