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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. . | Using Reinforcement Learning to Build a Better Model of Dialogue State Pittsburgh PA 15260 USA tetreaul@pitt.edu Joel R. Tetreault Diane J. Litman University of Pittsburgh University of Pittsburgh Learning Research and Development Center Department of Computer Science Learning Research and Development Center Pittsburgh PA 15260 USA litman@cs.pitt.edu Abstract 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. 1 Introduction This paper presents initial research toward the long-term goal of designing a tutoring system that can effectively adapt to the student. While most work in Markov Decision Processes MDPs and spoken dialogue have focused on building better policies Walker 2000 Henderson et al. 2005 to date very little empirical work has tested the utility of adding specialized features to construct a better dialogue state. We wish to show that adding more complex factors to a representation of student state is a worthwhile pursuit since it alters what action the tutor should make. The five dialogue factors we explore are dialogue acts certainty level frustration level concept repetition and student performance. All five are factors that are not just unique to the tutoring domain but are important to dialogue systems in general. Our results show that using these features combined with the common baseline
