Automatic optimization of dialogue management

Abstract

Designing the dialogue strategy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue strategy that addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We then show that our approach measurably improves performance in an experimental system.