ER-Chat: A Text-to-Text Open-Domain Dialogue Framework for Emotion Regulation

Shin Katayama, Shunsuke Aoki, Takuro Yonezawa, Tadashi Okoshi, Jin Nakazawa, Nobuo Kawaguchi

Research output: Contribution to journalArticlepeer-review

Abstract

Emotions are essential for constructing social relationships between humans and interactive systems. Although emotional and empathetic dialogue generation methods have been proposed for dialogue systems, appropriate dialogue involves not only mirroring emotions and always being empathetic but also complex factors such as context. This paper proposes Emotion Regulation Chat (ER-Chat) as an end-to-end dialogue framework for emotion regulation. Emotion regulation is concerned with actions to approach appropriate emotional states. Learning appropriate emotion and intent when responding on the basis of the context of the dialogue enables the generation of more human-like dialogue. We conducted automatic and human evaluations to demonstrate the superiority of ER-Chat over the baseline system. The results show that inclusion of emotion and intent prediction mechanisms enable generation of dialogues with greater fluency, diversity, emotion awareness, and emotion appropriateness that are greatly preferred by humans.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalIEEE Transactions on Affective Computing
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Affective Computing
  • Context modeling
  • Decoding
  • Dialogue Generation
  • Emotion recognition
  • Emotion Regulation
  • Labeling
  • Regulation
  • Task analysis
  • Transformers

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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