Paper: Using Reinforcement Learning To Build A Better Model Of Dialogue State

ACL ID E06-1037
Title Using Reinforcement Learning To Build A Better Model Of Dialogue State
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

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 RLto construct abetter 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.