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Modeling spoken decision support dialogue and optimization of its dialogue strategy

Modeling spoken decision support dialogue and optimization of its dialogue strategy Modeling Spoken Decision Support Dialogue and Optimization of Its Dialogue Strategy TERUHISA MISU, KOMEI SUGIURA, TATSUYA KAWAHARA, KIYONORI OHTAKE, CHIORI HORI, HIDEKI KASHIOKA, HISASHI KAWAI, and SATOSHI NAKAMURA, National Institute of Information and Communications Technology This article presents a user model for user simulation and a system state representation in spoken decision support dialogue systems. When selecting from a group of alternatives, users apply different decision-making criteria with different priorities. At the beginning of the dialogue, however, users often do not have a de nite goal or criteria in which they place value, thus they can learn about new features while interacting with the system and accordingly create new criteria. In this article, we present a user model and dialogue state representation that accommodate these patterns by considering the user ™s knowledge and preferences. To estimate the parameters used in the user model, we implemented a trial sightseeing guidance system, collected dialogue data, and trained a user simulator. Since the user parameters are not observable from the system, the dialogue is modeled as a partially observable Markov decision process (POMDP), and a dialogue state representation was introduced based on the model. We then optimized its dialogue strategy so http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/1966407.1966415
Publisher site
See Article on Publisher Site

Abstract

Modeling Spoken Decision Support Dialogue and Optimization of Its Dialogue Strategy TERUHISA MISU, KOMEI SUGIURA, TATSUYA KAWAHARA, KIYONORI OHTAKE, CHIORI HORI, HIDEKI KASHIOKA, HISASHI KAWAI, and SATOSHI NAKAMURA, National Institute of Information and Communications Technology This article presents a user model for user simulation and a system state representation in spoken decision support dialogue systems. When selecting from a group of alternatives, users apply different decision-making criteria with different priorities. At the beginning of the dialogue, however, users often do not have a de nite goal or criteria in which they place value, thus they can learn about new features while interacting with the system and accordingly create new criteria. In this article, we present a user model and dialogue state representation that accommodate these patterns by considering the user ™s knowledge and preferences. To estimate the parameters used in the user model, we implemented a trial sightseeing guidance system, collected dialogue data, and trained a user simulator. Since the user parameters are not observable from the system, the dialogue is modeled as a partially observable Markov decision process (POMDP), and a dialogue state representation was introduced based on the model. We then optimized its dialogue strategy so

Journal

ACM Transactions on Speech and Language Processing (TSLP)Association for Computing Machinery

Published: May 1, 2011

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