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Using reinforcement learning to build a better model of dialogue state

Tetreault, JR and Litman, DJ (2006) Using reinforcement learning to build a better model of dialogue state. In: UNSPECIFIED.

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Given the growing complexity of tasks that spoken dialogue systems are trying to handle, Reinforcement Learning (RL) has been increasingly used as a way of automatically learning the best policy for a system to make. While most work has focused on generating better policies for a dialogue manager, very little work has been done in using RL to construct a better dialogue state. This paper presents a RL approach for determining what dialogue features are important to a spoken dialogue tutoring system. Our experiments show that incorporating dialogue factors such as dialogue acts, emotion, repeated concepts and performance play a significant role in tutoring and should be taken into account when designing dialogue systems.


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Tetreault, JR
Litman, DJdlitman@pitt.eduDLITMAN
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 1 December 2006
Date Type: Publication
Journal or Publication Title: EACL 2006 - 11th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Page Range: 289 - 296
Event Type: Conference
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISBN: 1932432590, 9781932432596
Date Deposited: 05 Jan 2015 15:29
Last Modified: 02 Feb 2019 15:59


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