Reinforcement Learning for Spoken Dialogue Systems

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Satinder Singh, Michael Kearns, Diane Litman, Marilyn Walker


Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application ofMDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered from two dialogue systems built at AT&T Labs. Our experiments demonstrate that RLDS holds promise as a tool for "browsing" and understanding correlations in complex, temporally dependent dialogue corpora.