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Finite-to-Infinite N-Best POMDP for Spoken Dialogue Management

  • Guohua WuEmail author
  • Caixia Yuan
  • Bing Leng
  • Xiaojie Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9427)

Abstract

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.

Keywords

Partially Observable Markov Decision Process (POMDP) Spoken Dialogue System (SDS) Dynamic binding N-best 

Notes

Acknowledgments

This work was partially supported by National Natural Science Foundation of China (No.61273365, No.61202248), discipline building plan in 111 base (No.B08004) and Engineering Research Center of Information Networks, Ministry of Education.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Guohua Wu
    • 1
    Email author
  • Caixia Yuan
    • 1
  • Bing Leng
    • 1
  • Xiaojie Wang
    • 1
  1. 1.School of ComputerBeijing University of Posts and TelecommunicationsBeijingChina

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