Convolutional Neural Networks for Multi-topic Dialog State Tracking

  • Hongjie ShiEmail author
  • Takashi Ushio
  • Mitsuru Endo
  • Katsuyoshi Yamagami
  • Noriaki Horii
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 427)


The main task of the fourth Dialog State Tracking Challenge (DSTC4) is to track the dialog state by filling in various slots, each of which represents a major subject discussed in the dialog. In this article we focus on the ‘INFO’ slot that tracks the general information provided in a sub-dialog segment, and propose an approach for this slot-filling using convolutional neural networks (CNNs). Our CNN model is adapted to multi-topic dialog by including a convolutional layer with general and topic-specific filters. The evaluation on DSTC4 common test data shows that our approach outperforms all other submitted entries in terms of overall accuracy of the ‘INFO’ slot.


Dialog state tracking Convolutional neural networks Multi-topic dialog state tracking Domain adaptation Semi-supervised learning 


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Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  • Hongjie Shi
    • 1
    Email author
  • Takashi Ushio
    • 1
  • Mitsuru Endo
    • 1
  • Katsuyoshi Yamagami
    • 1
  • Noriaki Horii
    • 1
  1. 1.Intelligence Research Laboratory, Panasonic CorporationOsakaJapan

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