CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation

Jiashuo Wang, Yi Cheng, Wenjie Li


Abstract
Recent approaches to empathetic response generation incorporate emotion causalities to enhance comprehension of both the user’s feelings and experiences. However, these approaches suffer from two critical issues. First, they only consider causalities between the user’s emotion and the user’s experiences, and ignore those between the user’s experiences. Second, they neglect interdependence among causalities and reason them independently. To solve the above problems, we expect to reason all plausible causalities interdependently and simultaneously, given the user’s emotion, dialogue history, and future dialogue content. Then, we infuse these causalities into response generation for empathetic responses. Specifically, we design a new model, i.e., the Conditional Variational Graph Auto-Encoder (CVGAE), for the causality reasoning, and adopt a multi-source attention mechanism in the decoder for the causality infusion. We name the whole framework as CARE, abbreviated for CAusality Reasoning for Empathetic conversation. Experimental results indicate that our method achieves state-of-the-art performance.
Anthology ID:
2022.findings-emnlp.51
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
729–741
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.51
DOI:
10.18653/v1/2022.findings-emnlp.51
Bibkey:
Cite (ACL):
Jiashuo Wang, Yi Cheng, and Wenjie Li. 2022. CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 729–741, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation (Wang et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.51.pdf