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Contribution tracking: participating in task-oriented dialogue under uncertainty
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Contribution tracking: participating in task-oriented dialogue under uncertainty
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Participating in task-oriented dialogue under uncertainty
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DeVault
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David
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David DeVault
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Stone
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Matthew
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Advisory Committee
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Matthew Stone
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chair
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Michael
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Michael Littman
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Shan
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Chung-chieh
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Advisory Committee
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Chung-chieh Shan
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Traum
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David
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Advisory Committee
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David Traum
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Rutgers University
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Graduate School - New Brunswick
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theses
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2008
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2008-10
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English
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electronic
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xiii, 222 pages
Abstract
The 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.
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.
Note
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Ph.D.
Note
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Includes bibliographical references (p. 214-220).
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Computer Science
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Computational linguistics
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Discourse analysis
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Speech perception
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Graduate School - New Brunswick Electronic Theses and Dissertations
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http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.17459
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doi:10.7282/T35H7GMB
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ETD doctoral
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Open
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David DeVault
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Rutgers University. Graduate School - New Brunswick
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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