Dialog state tracking with attention-based sequence-to-sequence learning

Takaaki Hori, Hai Wang, Chiori Hori, Shinji Watanabe, Bret Harsham, Jonathan Le Roux, John R. Hershey, Yusuke Koji, Yi Jing, Zhaocheng Zhu, Takeyuki Aikawa

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

14 Citations (Scopus)

Abstract

We present an advanced dialog state tracking system designed for the 5th Dialog State Tracking Challenge (DSTC5). The main task of DSTC5 is to track the dialog state in a human-human dialog. For each utterance, the tracker emits a frame of slot-value pairs considering the full history of the dialog up to the current turn. Our system includes an encoder-decoder architecture with an attention mechanism to map an input word sequence to a set of semantic labels, i.e., slot-value pairs. This handles the problem of the unknown alignment between the utterances and the labels. By combining the attention-based tracker with rule-based trackers elaborated for English and Chinese, the F-score for the development set improved from 0.475 to 0.507 compared to the rule-only trackers. Moreover, we achieved 0.517 F-score by refining the combination strategy based on the topic and slot level performance of each tracker. In this paper, we also validate the efficacy of each technique and report the test set results submitted to the challenge.

Original languageEnglish
Title of host publication2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages552-558
Number of pages7
ISBN (Electronic)9781509049035
DOIs
Publication statusPublished - 2017 Feb 7
Externally publishedYes
Event2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - San Diego, United States
Duration: 2016 Dec 132016 Dec 16

Other

Other2016 IEEE Workshop on Spoken Language Technology, SLT 2016
CountryUnited States
CitySan Diego
Period16/12/1316/12/16

Keywords

  • Attention model
  • Dialog state tracking
  • Encoder-decoder
  • Long short-term memory
  • Sequence-to-sequence learning

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Artificial Intelligence
  • Language and Linguistics
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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  • Cite this

    Hori, T., Wang, H., Hori, C., Watanabe, S., Harsham, B., Le Roux, J., Hershey, J. R., Koji, Y., Jing, Y., Zhu, Z., & Aikawa, T. (2017). Dialog state tracking with attention-based sequence-to-sequence learning. In 2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings (pp. 552-558). [7846317] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SLT.2016.7846317