Paper: Comparing The Utility Of State Features In Spoken Dialogue Using Reinforcement Learning

ACL ID N06-1035
Title Comparing The Utility Of State Features In Spoken Dialogue Using Reinforcement Learning
Venue Human Language Technologies
Session Main Conference
Year 2006
Authors

Recent work in designing spoken dialogue systems has focused on using Reinforce- ment Learning to automatically learn the best action for a system to take at any point in the dialogue to maximize dia- logue success. While policy development is very important, choosing the best fea- tures to model the user state is equally im- portant since it impacts the actions a sys- tem should make. In this paper, we com- pare the relative utility of adding three fea- tures to a model of user state in the do- main of a spoken dialogue tutoring sys- tem. In addition, we also look at the ef- fects of these features on what type of a question a tutoring system should ask at any state and compare it with our previ- ous work on using feedback as the system action.