Using Reinforcement Learning to Build a Better Model of Dialogue State
EACL(2006)
摘要
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 au- tomatically 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 dia- logue tutoring system. Our experiments show that incorporating dialogue factors such as dialogue acts, emotion, repeated concepts and performance play a signifi- cant role in tutoring and should be taken into account when designing dialogue sys- tems.
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关键词
reinforcement learning
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