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Contribution tracking: participating in task-oriented dialogue under uncertainty
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DeVault, David. Contribution tracking: participating in task-oriented dialogue under uncertainty. Retrieved from https://doi.org/doi:10.7282/T35H7GMB

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Uniform TitleContribution tracking: participating in task-oriented dialogue under uncertainty
NameDeVault, David (author); Stone, Matthew (chair); Littman, Michael (internal member); Shan, Chung-chieh (internal member); Traum, David (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2008
Other Date2008-10 (degree)
SubjectComputer Science, Computational linguistics, Discourse analysis, Speech perception
Extentxiii, 222 pages
DescriptionThe contribution of this dissertation is to show how interlocutors in dialogue can reason probabilistically about natural language interpretation, dialogue state (context), and natural language generation in a way that is consistent with three fundamental claims made by mainstream theories of pragmatic reasoning in human-human dialogue: 1. interlocutors track and exploit the evolving context to coordinate their individual contributions; 2. the current context depends on what the previous utterances of both interlocutors have meant (contributed); 3. what a speaker can recognizably mean (contribute) by a specific choice of words depends on the current context.
Mainstream pragmatic theories depend on these assumptions to explain how a speaker can make linguistic choices that the hearer will interpret as intended, but these theories do not lend themselves to straightforward probabilistic reasoning. Engineering approaches to building dialogue systems implement straightforward probabilistic reasoning, but sacrifice one or more (sometimes all) of these fundamental aspects of pragmatic theory in order to do so. This dissertation shows how we can achieve the robustness and data-driven methodology enjoyed by engineering approaches while keeping our interlocutors on a sound theoretical footing, and thereby points the way toward a new class of dialogue systems that are empirically driven, that are robust pragmatic reasoners, and that exhibit human-like sensitivity to the ins and outs of language use in context.
NotePh.D.
NoteIncludes bibliographical references (p. 214-220).
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T35H7GMB
LanguageEnglish
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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