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

Ryota Yahagi, Yuya Chiba, Takashi Nose, Akinori Ito

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

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages349-350
Number of pages2
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

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

Keywords

  • response generation
  • spoken dialogue system

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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