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2010. Bayesian update of dialogue state: A pomdp framework for spoken dialogue systems
| Content Provider | CiteSeerX |
|---|---|
| Author | Thomson, Blaise Young, Steve |
| Abstract | 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 using Bayesian updates of the dialogue state significantly outperforms traditional definitions of the dialogue state. Policy learning worked effectively and the learned policy outperformed all others on simulations. In user trials the learned policy was also competitive, although its optimality was less conclusive. Overall, the Bayesian update of dialogue state framework was shown to be a feasible and effective approach to building real-world POMDP-based dialogue systems. |
| File Format | |
| Journal | Computer Speech & Language |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Spoken Dialogue System Bayesian Update Dialogue State Pomdp Framework Learned Policy User Trial Loopy Belief Propagation Algorithm Approximate Method Original Algorithm Spoken Dialogue Management Tractable Method Policy Learning Dialogue State Framework Traditional Definition Pomdp-based Dialogue State Updating Approach Various Simplification Real-world Pomdp-based Dialogue System Effective Approach Observable Markov Decision Process Real-time Dialogue State Update Pomdp Model Factorised Policy Statistical Model Episodic Natural Actor Critic Algorithm Exact Belief State Update Motivated Framework Second Contribution |
| Content Type | Text |
| Resource Type | Article |
