Automatic Optimization of Dialogue Management

Designing the dialogue strategy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing dialogue strategy. We first present a practical methodology that addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We then demonstrate how we have used this methodology to measurably improve performance in a large-scale experimental system.

• Type: Conference
• Year: 2000
• Where: COLING
• Authors: Diane J. Litman, Michael S. Kearns, Satinder P. Singh, Marilyn A. Walker
• Keywords: COLING 2000, COLING 2008, Dialogue Strategy, Dialogue System, Spoken Dialogue System