MuSE: A Multi-scale Emotional Flow Graph Model for Empathetic Dialogue Generation

Deji Zhao, Donghong Han*, Ye Yuan, Chao Wang, Shuangyong Song

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

The purpose of empathetic dialogue generation is to fully understand the speakers’ emotional needs in dialogues and to generate appropriate empathetic responses. Existing works mainly focus on the overall coarse-grained emotion of the context while neglecting different utterances’ fine-grained emotions, which leads to the inability to detect the speakers’ fine-grained emotional changes during a conversation. However, in real-life dialogue scenarios, the speaker usually carries an initial emotional state that changes continuously during the conversation. Therefore, understanding a series of emotional states can help to better understand speakers’ emotions and generate empathetic responses. To address this issue, we propose a Multi-Scale Emotional flow model called MuSE, which simulates speakers’ emotional flow. First, we introduce a fine-grained expansion strategy to transform context into an emotional flow graph that combines multi-scale coarse and fine-grained information. This emotional flow graph captures speakers’ constant emotional changes at each turn of a conversation. And then, the emotion node and the situational node are introduced to the emotional flow graph respectively in order to extend the speakers’ initial emotion into the ensuing conversation. Finally, we conduct experiments on the public EMPATHETIC DIALOGUES dataset. The experimental results demonstrate that the MuSE model achieves superior performance under both automatic evaluation and human evaluation metrics compared with the existing baseline models. Our code is available at https://github.com/DericZhao/MuSE.

源语言英语
主期刊名Machine Learning and Knowledge Discovery in Databases
主期刊副标题Research Track - European Conference, ECML PKDD 2023, Proceedings
编辑Danai Koutra, Claudia Plant, Manuel Gomez Rodriguez, Elena Baralis, Francesco Bonchi
出版商Springer Science and Business Media Deutschland GmbH
491-507
页数17
ISBN(印刷版)9783031434143
DOI
出版状态已出版 - 2023
活动European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, 意大利
期限: 18 9月 202322 9月 2023

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
14170 LNAI
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
国家/地区意大利
Turin
时期18/09/2322/09/23

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