Semantics of dialogue

Ginzburg, Jonathan

doi:10.1017/cbo9781139236157.006

Cited by 69 publications

(61 citation statements)

References 58 publications

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“…Interested readers are referred to Thomason 1990 andRoberts 2004 for general introductions. Influential foundational work in this area includes the papers collected in Stalnaker 1998; the diverse approaches to modeling common ground in Gauker 1998, Gunlogson 2001, and Farkas & Bruce 2010; the theory of indexicals in Kaplan 1978Kaplan , 1989; the dynamic approaches of Kamp 1981 andHeim 1982; the question-driven models of Roberts 1996 andGinzburg 1996; and the goal-driven models of Perrault & Allen 1980, Allen 1991, Benz et al 2005b, and Stone et al 2007.…”

Section: Presuppositionmentioning

confidence: 99%

“…Assuming cooperativity, indirect answers of this form give rise to additional conversational inferences about the set of questions (issues, goals) in play in the discourse (Ginzburg, 1996;Roberts, 1996;Büring, 1999;Büring, 2003;de Marneffe et al, 2010). This too is relevance-based; once the conversational implicature is taken into account, Bill in fact over-answers the direct question.…”

Section: Relevance Implicaturesmentioning

confidence: 99%

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Presupposition and Implicature

Potts¹

2015

The Handbook of Contemporary Semantic Theory

178

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Section: Presuppositionmentioning

confidence: 99%

Section: Relevance Implicaturesmentioning

confidence: 99%

Presupposition and Implicature

Potts¹

2015

The Handbook of Contemporary Semantic Theory

178

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“…These collective attention concepts might bring to mind the concept of a Question Under Discussion (QUD; Roberts 1996Roberts , 2012Ginzburg 1996Ginzburg , 2012, a contextually salient discourse topic that interlocutors are collaboratively answering. 9 But while we think that QUDs are intimately related to our group attention concepts, especially to public attention, there are good theoretical reasons to distinguish these concepts from one another.…”

Section: Attention In Discoursementioning

confidence: 99%

Epistemic Resistance Moves

Bledin

Rawlins

2016

SALT

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This paper introduces and analyzes a new kind of non-acceptance, nondisagreeing move: resistance. We focus in particular on attention-targeted resistance facilitated by epistemic possibility claims. In this response type, we suggest, an agent draws attention to some subsidiary issue that they think might cause an interlocutor to withdraw a previous commitment. We develop a granularity model of attention where drawing attention in discourse can refine the space of possibilities under consideration and consequently lead to changes in view.

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“…It has also been used for a corpus of in-car MP3 player dialogues (Rieser et al 2005a(Rieser et al , 2005bLemon 2006, 2008) and has been adopted for the DiSCoH project (Andreani et al 2006), but in general there are several other features one might want to include in dialogue context annotations. For the dialogue level, such features include changes to issues/questions under discussion (Ginzburg 1996), changes to common ground (Clark and Brennan 1991;Traum 1994), obligations (Traum and Allen 1994), system and user intentions (Grosz and Sidner 1986), syntactic and semantic parses, and salient NPs. For the task level, such features include the number of database query results (for slotfilling/information-seeking dialogues), user goals, and confidence in each slot (e.g.…”

Section: The Talk Context Annotation Schemementioning

confidence: 99%

Automatic annotation of context and speech acts for dialogue corpora

Georgila¹,

Lemon²,

Henderson³

et al. 2009

Nat. Lang. Eng.

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Richly annotated dialogue corpora are essential for new research directions in statistical learning approaches to dialogue management, context-sensitive interpretation, and contextsensitive speech recognition. In particular, large dialogue corpora annotated with contextual information and speech acts are urgently required. We explore how existing dialogue corpora (usually consisting of utterance transcriptions) can be automatically processed to yield new corpora where dialogue context and speech acts are accurately represented. We present a conceptual and computational framework for generating such corpora. As an example, we present and evaluate an automatic annotation system which builds 'Information State Update' (ISU) representations of dialogue context for the Communicator (2000 and 2001) corpora of human-machine dialogues (2,331 dialogues). The purposes of this annotation are to generate corpora for reinforcement learning of dialogue policies, for building user simulations, for evaluating different dialogue strategies against a baseline, and for training models for contextdependent interpretation and speech recognition. The automatic annotation system parses system and user utterances into speech acts and builds up sequences of dialogue context representations using an ISU dialogue manager. We present the architecture of the automatic annotation system and a detailed example to illustrate how the system components interact to produce the annotations. We also evaluate the annotations, with respect to the task completion metrics of the original corpus and in comparison to hand-annotated data and annotations produced by a baseline automatic system. The automatic annotations perform well and largely outperform the baseline automatic annotations in all measures. The resulting annotated corpus has been used to train high-quality user simulations and to learn successful dialogue strategies. The final corpus will be made publicly available.

show abstract

Semantics of dialogue

Cited by 69 publications

References 58 publications

Presupposition and Implicature

Presupposition and Implicature

Epistemic Resistance Moves

Automatic annotation of context and speech acts for dialogue corpora

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