雑談システムにおけるTwitterデータからの統計的バックチャネル応答抽出手法 Statistical Backchannel Extraction from Twitter Data in Casual Dialogue System

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著者

    • 福田 拓也 Fukuda Takuya
    • 筑波大学大学院図書館情報メディア研究科 Graduate School of Library, Information and Media Studies, University of Tsukuba
    • 若林 啓 Wakabayashi Kei
    • 筑波大学図書館情報メディア系 Faculty of Library, Information and Media Science, University of Tsukuba

抄録

<p>The backchannel plays an important role in smooth communication. For dialogue system, appropriate backchanneling is a significant factor that makes more natural conversation. However, many existing dialogue systems have poor backchannel patterns and only can produce simple responses. In this paper, we propose a method to extract various backchannels that are suitable for user utterance with no restriction of the diversity of backchannels. We conduct an experiment that compares the proposed method with two existing methods; a classification-based method and a simple extraction-based method with a message length limit. The generated responses are evaluated by human workers. The result shows that the proposed method generates backchannels that are highly diverse and more appropriate in terms of the response to the user utterance.</p>

収録刊行物

  • 人工知能学会論文誌

    人工知能学会論文誌 33(1), DSH-H_1-10, 2018

    一般社団法人 人工知能学会

各種コード

  • NII論文ID(NAID)
    130006302226
  • 本文言語コード
    JPN
  • ISSN
    1346-0714
  • データ提供元
    J-STAGE 
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