Using Reinforcement Learning to Build a Better Model of Dialogue State

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.

• Type: Conference
• Year: 2006
• Where: EACL
• Authors: Joel R. Tetreault, Diane J. Litman
• Keywords: Dialogue, Dialogue Systems, EACL 2006, Natural Language Processing, Spoken Dialogue