Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems

This paper describes a statistically motivated framework for performing real-time dialogue state updates and policy learning in a spoken dialogue system. The framework is based on the partially observable Markov decision process (POMDP), which provides a well-founded, statistical model of spoken dialogue management. However, exact belief state updates in a POMDP model are computationally intractable so approximate methods must be used. This paper presents a tractable method based on the loopy belief propagation algorithm. Various simplifications are made, which improve the efficiency significantly compared to the original algorithm as well as compared to other POMDP-based dialogue state updating approaches. A second contribution of this paper is a method for learning in spoken dialogue systems which uses a factorised policy with the episodic Natural Actor Critic algorithm. The framework proposed in this paper was tested on both simulations and in a user trial. Both indicated that usin...

• Type: Journal
• Year: 2010
• Where: CSL
• Authors: Blaise Thomson, Steve Young
• Keywords: Automated Reasoning, CSL 2010, Dialogue, Dialogue State, Dialogue Systems