Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System

Details Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System

2011-06-03

arxiv.org/abs/1106.0676v1

Journal Of Artificial Intelligence Research, Volume 16, pages 105-133, 2002

Computer Science

Learning

Scientific paper

10.1613/jair.859

Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and empirical evaluation of NJFun, an experimental spoken dialogue system that provides users with access to information about fun things to do in New Jersey. Our results show that by optimizing its performance via reinforcement learning, NJFun measurably improves system performance.

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