Finite-to-Infinite N-Best POMDP for Spoken Dialogue Management
Guohua Wu, Caixia Yuan, Bing Leng, Xiaojie Wang · Lecture notes in computer science · 2015
Partially Observable Markov Decision Process (POMDP) has been widely used as dialogue management in slot-filling Spoken Dialogue System (SDS). But there are still lots of open problems. The contribution of this paper lies in two aspects. Firstly, the observation probability of POMDP is estimated from the N-Best list of Automatic Speech Recognition (ASR) rather than the top one. This modification gives SDS a chance to address the uncertainty of ASR. Secondly, a dynamic binding technique is proposed for slots with infinite values so as to deal with uncertainty of talking object. The proposed methods have been implemented on a teach-and-learn spoken dialogue system. Experimental results show that performance of system improves significantly by introducing the proposed methods.