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An Effective Adaptive Dialogue Strategy Using Reinforcement Loaming  

Kim, Won-Il (삼성전자 영상디스플레이 사업부)
Ko, Young-Joong (동아대학교 컴퓨터공학과)
Seo, Jung-Yun (서강대학교 컴퓨터학과)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.1, 2008 , pp. 33-40 More about this Journal
Abstract
In this paper, we propose a method to enhance adaptability in a dialogue system using the reinforcement learning that reduces response errors by trials and error-search similar to a human dialogue process. The adaptive dialogue strategy means that the dialogue system improves users' satisfaction and dialogue efficiency by loaming users' dialogue styles. To apply the reinforcement learning to the dialogue system, we use a main-dialogue span and sub-dialogue spans as the mathematic application units, and evaluate system usability by using features; success or failure, completion time, and error rate in sub-dialogue and the satisfaction in main-dialogue. In addition, we classify users' groups into beginners and experts to increase users' convenience in training steps. Then, we apply reinforcement learning policies according to users' groups. In the experiments, we evaluated the performance of the proposed method on the individual reinforcement learning policy and group's reinforcement learning policy.
Keywords
Dialogue System; Adaptive Dialogue Strategy; Reinforcement Learning; Main-dialogue and Sub-dialogue; Q-learning;
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