Optimizing generative dialog state tracker via cascading gradient descent

Byung Jun Lee, Woosang Lim, Daejoong Kim, Kee Eung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

For robust spoken dialog management, various dialog state tracking methods have been proposed. Although discriminative models are gaining popularity due to their superior performance, generative models based on the Partially Observable Markov Decision Process model still remain attractive since they provide an integrated framework for dialog state tracking and dialog policy optimization. Although a straightforward way to fit a generative model is to independently train the component probability models, we present a gradient descent algorithm that simultaneously train all the component models. We show that the resulting tracker performs competitively with other top-performing trackers that participated in DSTC2.

Original languageEnglish
Title of host publicationSIGDIAL 2014 - 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages273-281
Number of pages9
ISBN (Electronic)9781941643211
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2014 - Philadelphia, United States
Duration: 2014 Jun 182014 Jun 20

Publication series

NameSIGDIAL 2014 - 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference

Conference

Conference15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2014
Country/TerritoryUnited States
CityPhiladelphia
Period14/6/1814/6/20

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Modelling and Simulation
  • Human-Computer Interaction

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