TriKF
Triple-Perspective Knowledge Fusion Network for Empathetic Question Generation
Bibliographic Data
| ID | 22107827 |
|---|---|
| Authors | Tiantian Chen (0000-0003-3513-6170, Tongji University), Ying Shen (0000-0001-8541-6010, Tongji University), Xuri Chen (0009-0000-9426-2703, Tongji University), Lin Zhang (0000-0001-5966-3827, Tongji University), Shengjie Zhao (0000-0003-0187-2049, Tongji University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 6 |
| Pages | 7186-7199 |
| Publication date | 2024-12-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3418820 |
| OpenAlex | W4400579209 |
| Language | EN |
| References cited | 53 |
Questioning is one of the essential tactics for demonstrating empathy in social dialogues. Effective questioning can guide individuals to express their experiences, feelings, and thoughts, aiming to establish emotional connections and deepen interpersonal understanding. However, how to generate empathetic questions in emotional support conversations remains an unresolved issue. To fill this research gap to some extent, we propose an empathetic question generation (QG) framework called triple-perspective knowledge fusion (TriKF), which incorporates external knowledge from the perspectives of events, cognition, and affection to comprehensively understand the dialogue context. Specifically, this framework acquires commonsense knowledge from these three perspectives and integrates them into the dialogue context to enrich the contextual information. To the best of our knowledge, this is the first method proposed for empathetic QG. Additionally, we construct an empathetic question dataset, namely EQ-EMAC. This dataset comprises 4213 dialogues with single user inputs and multiple empathetic question responses, which can be utilized to assess the effectiveness and generalization capability of empathetic QG models. Experimental results have demonstrated the effectiveness of TriKF on the task of empathetic QG compared with seven baseline models
Fusion · Computer Science · Educational Technology and Assessment · Knowledge Management and Technology · Philosophy · Psychology · Robotics and Automated Systems · Artificial Intelligence
| Highly cited | No |
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