CUED Publications database

Parameter learning for POMDP spoken dialogue models

Thomson, B and Jurčíček, F and Gašić, M and Keizer, S and Mairesse, F and Yu, K and Young, S (2010) Parameter learning for POMDP spoken dialogue models. 2010 IEEE Workshop on Spoken Language Technology, SLT 2010 - Proceedings. pp. 271-276.

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Abstract

The partially observable Markov decision process (POMDP) provides a popular framework for modelling spoken dialogue. This paper describes how the expectation propagation algorithm (EP) can be used to learn the parameters of the POMDP user model. Various special probability factors applicable to this task are presented, which allow the parameters be to learned when the structure of the dialogue is complex. No annotations, neither the true dialogue state nor the true semantics of user utterances, are required. Parameters optimised using the proposed techniques are shown to improve the performance of both offline transcription experiments as well as simulated dialogue management performance. ©2010 IEEE.

Item Type: Article
Uncontrolled Keywords: Dialogue management Expectation propagation POMDP Spoken language understanding
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 12:12
Last Modified: 22 Dec 2014 01:20
DOI: 10.1109/SLT.2010.5700863