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

研究成果: Conference contribution

17 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ552-558
ページ数7
ISBN(電子版)9781509049035
DOI
出版ステータスPublished - 2017 2月 7
外部発表はい
イベント2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - San Diego, United States
継続期間: 2016 12月 132016 12月 16

出版物シリーズ

名前2016 IEEE Workshop on Spoken Language Technology, SLT 2016 - Proceedings

Other

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

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • 人工知能
  • 言語および言語学
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

フィンガープリント

「Dialog state tracking with attention-based sequence-to-sequence learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル