Automatically Select Emotion for Response via Personality-affected Emotion Transition

Zhiyuan WEN, Jiannong CAO, Ruosong YANG, Shuaiqi LIU, Jiaxing SHEN

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Research

1 Citation (Scopus)

Abstract

To provide consistent emotional interaction with users, dialog systems should be capable to automatically select appropriate emotions for responses like humans. However, most existing works focus on rendering specified emotions in responses or empathetically respond to the emotion of users, yet the individual difference in emotion expression is overlooked. This may lead to inconsistent emotional expressions and disinterest users. To tackle this issue, we propose to equip the dialog system with personality and enable it to automatically select emotions in responses by simulating the emotion transition of humans in conversation. In detail, the emotion of the dialog system is transitioned from its preceding emotion in context. The transition is triggered by the preceding dialog context and affected by the specified personality trait. To achieve this, we first model the emotion transition in the dialog system as the variation between the preceding emotion and the response emotion in the Valence-Arousal-Dominance (VAD) emotion space. Then, we design neural networks to encode the preceding dialog context and the specified personality traits to compose the variation. Finally, the emotion for response is selected from the sum of the preceding emotion and the variation. We construct a dialog dataset with emotion and personality labels and conduct emotion prediction tasks for evaluation. Experimental results validate the effectiveness of the personality-affected emotion transition.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
PublisherAssociation for Computational Linguistics (ACL)
Pages5010-5020
Number of pages11
ISBN (Electronic)9781954085541
DOIs
Publication statusPublished - Aug 2021
Externally publishedYes
EventFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online
Duration: 1 Aug 20216 Aug 2021

Conference

ConferenceFindings of the Association for Computational Linguistics: ACL-IJCNLP 2021
CityVirtual, Online
Period1/08/216/08/21

Bibliographical note

This work is supported by the Hong Kong RGC Collaborative Research Fund with project code C6030-18G and Hong Kong Red Swastika Society Tai Po Secondary School with project code P20-0021.

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