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ACL Anthology

Using Reinforcement Learning to Build a Better Model of Dialogue State

11 years 6 months ago
Using Reinforcement Learning to Build a Better Model of Dialogue State
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.
Joel R. Tetreault, Diane J. Litman
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where EACL
Authors Joel R. Tetreault, Diane J. Litman
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