Incremental response generation using prefix-to-prefix model for dialogue system

Ryota Yahagi, Yuya Chiba, Takashi Nose, Akinori Ito

研究成果: Conference contribution

抄録

A spoken dialogue system that is currently deployed in many devices cannot respond to a user with a natural switching pause. One of the reasons is that the conventional system generates the response with the pipe-line of several processes, such as speech recognition, response generation, and speech synthesis. The dialogue system should process the user's utterance and generate the response incrementally to achieve natural turn-taking as human-being. In this paper, we examined an incremental response generation method based on a Prefix-to-Prefix model, which is proposed for simultaneous machine translation. This model has a similar structure with the Sequence-to-Sequence model, which is successfully applied to the response generation. We conducted several experiments to confirm the effectiveness of the Prefix-to-Prefix model for incremental response generation.

本文言語English
ホスト出版物のタイトル2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ349-350
ページ数2
ISBN(電子版)9781728198026
DOI
出版ステータスPublished - 2020 10 13
イベント9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
継続期間: 2020 10 132020 10 16

出版物シリーズ

名前2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
国/地域Japan
CityKobe
Period20/10/1320/10/16

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学
  • メディア記述
  • 器械工学
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識

フィンガープリント

「Incremental response generation using prefix-to-prefix model for dialogue system」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