Paper: Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email

ACL ID C98-2214
Title Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email
Venue International Conference on Computational Linguistics
Session Main Conference
Year 1998
Authors

This paper describes a novel method by which a dia- logue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formulated in terms of com- municative intentions, there has been little work on automatically optimizing an agent's choices when there are multiple ways to realize a communica- tive intention. Our method is based on a combina- tion of learning algorithms and empirical evaluation techniques. The learning component of our method is based on algorithms for reinforcement learning, such as dynamic programming and Q-learning. The empirical component uses the PARADISE evalua- tion framework (Walker et al., 1997) to identify the important peribrmance factors and to provide the performance function needed by t...