Healey et al. use experiments with chat dialogues to test the hypothesis that language co‐ordination is driven by ‘running repairs’. They replace signals of understanding such as “okay” with weaker, ‘spoof’ signals like “ummm”, and replace specific requests for clarification like “on the left?” with signals that suggest a higher degree of misunderstanding like “what?”. The latter manipulation causes participants to switch rapidly to more abstract forms of referring expression.

Feedback such as backchannels and clarification requests often occurs subsententially, demonstrating the incremental nature of grounding in dialogue. However, although such feedback can occur at any point within an utterance, it typically does not do so, tending to occur at Feedback Relevance Spaces. We present a corpus study of acknowledgements and clarification requests in British English, and describe how our low-level, semantic processing model in Dynamic Syntax accounts for this feedback. The model trivially accounts for the 85% of cases where (...) feedback occurs at FRSs, but we also describe how it can be integrated or interpreted at non-FRSs using the predictive, incremental and interactive nature of the formalism. This model shows how feedback serves to continually realign processing contexts and thus manage the characteristic divergence and convergence that is key to moving dialogue forward. (shrink)

Dynamic Syntax is an action-based grammar formalism which models the process of natural language understanding as monotonic tree growth. This paper presents an introduction to the notions of incrementality and underspecification and update, drawing on the assumptions made by DS. It lays out the tools of the theoretical framework that are necessary to understand the accounts developed in the other contributions to the Special Issue. It also represents an up-to-date account of the framework, combining the developments that have previously remained (...) distributed in a diverse body of literature. (shrink)

Miscommunication phenomena such as repair in dialogue are important indicators of the quality of communication. Automatic detection is therefore a key step toward tools that can characterize communication quality and thus help in applications from call center management to mental health monitoring. However, most existing computational linguistic approaches to these phenomena are unsuitable for general use in this way, and particularly for analyzing human–human dialogue: Although models of other-repair are common in human-computer dialogue systems, they tend to focus on specific (...) phenomena, missing the range of repair and repair initiation forms used by humans; and while self-repair models for speech recognition and understanding are advanced, they tend to focus on removal of “disfluent” material important for full understanding of the discourse contribution, and/or rely on domain-specific knowledge. We explain the requirements for more satisfactory models, including incrementality of processing and robustness to sparsity. We then describe models for self- and other-repair detection that meet these requirements and investigate how they perform on datasets from a range of dialogue genres and domains, with promising results. (shrink)

We present empirical evidence from dialogue that challenges some of the key assumptions in the Pickering & Garrod (P&G) model of speaker-hearer coordination in dialogue. The P&G model also invokes an unnecessarily complex set of mechanisms. We show that a computational implementation, currently in development and based on a simpler model, can account for more of this type of dialogue data.

Social robots have limited social competences. This leads us to view them as depictions of social agents rather than actual social agents. However, people also have limited social competences. We argue that all social interaction involves the depiction of social roles and that they originate in, and are defined by, their function in accounting for failures of social competence.

Interacting with others frequently involves making common-sense inferences linking context, background knowledge, and beliefs to utterances in the dialogue. As language users we are generally good at this kind of dialogical reasoning, and might not even be aware we are involved in it while we engage in a conversation. However, sometimes it is not obvious how a particular contribution should be interpreted in terms of the underpinning assumptions warranting an inference. In dialogue involving participants who demonstrate atypical linguistic behavior, such (...) as patients with schizophrenia, the effects may be even more marked. (shrink)

Empirical evidence from dialogue, both corpus and experimental, highlights the importance of interaction in language use – and this raises some questions for Christiansen & Chater's proposals. We endorse C&C's call for an integrated framework but argue that their emphasis on local, individual production and comprehension makes it difficult to accommodate the ubiquitous, interactive, and defeasible processes of clarification and repair in conversation.

In this paper we treat humorous situations as a series of events underpinned by topoi, principles of reasoning recognised within a socio-cultural community. We claim that humorous effect in jokes and other discourse is often created by the juxtaposition of topoi evoked. A prerequisite for this is that there is a shift where the interpreter of the discourse updates their information state with regard to a second topos being evoked. This view of humour is consistent with an incremental analysis of (...) dialogue, and we therefore argue that interaction is central both for humour creation and interpretation. We point out some different ways in which topoi are juxtaposed in humorous dialogues as well as in jokes published in social media or in joke books, and take jokes from the coronavirus pandemic as an example because this makes lots of new topoi available and therefore offers the opportunity of creating novel jokes based on the juxtaposition of the new and existing topoi. We explore how the mechanisms of inference in dialogue can be applied to humour through the four elements from our title: old, new, borrowed and taboo. (shrink)