One of the best known claims about human communication is that people's behaviour and language use converge during conversation. It has been proposed that these patterns can be explained by automatic, cross-person priming. A key test case is structural priming: does exposure to one syntactic structure, in production or comprehension, make reuse of that structure (by the same or another speaker) more likely? It has been claimed that syntactic repetition caused by structural priming is ubiquitous in conversation. However, previous work has not tested for general syntactic repetition effects in ordinary conversation independently of lexical repetition. Here we analyse patterns of syntactic repetition in two large corpora of unscripted everyday conversations. Our results show that when lexical repetition is taken into account there is no general tendency for people to repeat their own syntactic constructions. More importantly, people repeat each other's syntactic constructions less than would be expected by chance; i.e., people systematically diverge from one another in their use of syntactic constructions. We conclude that in ordinary conversation the structural priming effects described in the literature are overwhelmed by the need to actively engage with our conversational partners and respond productively to what they say.

Spoken contributions in dialogue often continue or complete earlier contributions by either the same or a different speaker. These compound contributions (CCs) thus provide a natural context for investigations of incremental processing in dialogue.We present a corpus study which confirms that CCs are a key dialogue phenomenon: almost 20% of contributions fit our general definition of CCs, with nearly 3% being the cross-person case most often studied. The results suggest that processing is word-by-word incremental, as splits can occur within syntactic ‘constituents’; however, some systematic differences between same- and cross-person cases indicate important dialogue-specific pragmatic effects. An experimental study then investigates these effects by artificially introducing CCs into multi-party text dialogue. Results suggest that CCs affect people’s expectations about who will speak next and whether other participants have formed a coalition or ‘party’.Together, these studies suggest that CCs require an incremental processing mechanism that can provide a resource for constructing linguistic constituents that span multiple contributions and multiple participants. They also suggest the need to model higher-level dialogue units that have consequences for the organization of turn-taking and for the development of a shared context.

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 (e.g., repair initiation by systems), 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 (for the former, an adaptation of an existing repair model; for the latter, an adaptation of standard techniques) and investigate how they perform on datasets from a range of dialogue genres and domains, with promising results.

Background Psychiatrists' questions are the mechanism for achieving clinical objectives and managing the formation of a therapeutic alliance – consistently associated with patient adherence. No research has examined the nature of this relationship and the different practices used in psychiatry. Questions are typically defined in binary terms (e.g. ‘open’ v. ‘closed’) that may have limited application in practice. Aims To undertake a detailed examination of the types of questions psychiatrists ask patients and explore their association with the therapeutic alliance and patient adherence. Method A coding protocol was developed to classify questions from 134 out-patient consultations, predominantly by syntactic form. Bivariate correlations with measures of patient adherence and the therapeutic alliance (psychiatrist-rated) were examined and assessed using generalised estimating equations, adjusting for patient symptoms, psychiatrist identity and amount of speech. Results Psychiatrists used only four of ten question types regularly: yes/no auxiliary questions, ‘wh-’ questions, declarative questions and tag questions. Only declarative questions predicted better adherence and perceptions of the therapeutic relationship. Conversely, ‘wh-’ questions – associated with positive symptoms – predicted poorer perceptions of the therapeutic relationship. Declarative questions were frequently used to propose an understanding of patients' experiences, in particular their emotional salience for the patient. Conclusions A refined defining of questioning practices is necessary to improve communication in psychiatry. The use of declarative questions may enhance alliance and adherence, or index their manifestation in talk, e.g. better mutual understanding. The function of ‘so’-prefaced declaratives, also used in psychotherapy, is more nuanced than negatively connotated ‘leading’ questions. Hearable as displays of empathy, they attend closely to patient experience, while balancing the tasks of assessment and treatment.

Previous research shows that aspects of doctor-patient communication in therapy can predict patient symptoms, satisfaction and future adherence to treatment (a significant problem with conditions such as schizophrenia). However, automatic prediction has so far shown success only when based on low-level lexical features, and it is unclear how well these can generalize to new data, or whether their effectiveness is due to their capturing aspects of style, structure or content. Here, we examine the use of topic as a higher-level measure of content, more likely to generalize and to have more explanatory power. Investigations show that while topics predict some important factors such as patient satisfaction and ratings of therapy quality, they lack the full predictive power of lower-level features. For some factors, unsupervised methods produce models comparable to manual annotation.