@inproceedings{wang-etal-2025-ecc,
title = "{ECC}: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation",
author = "Wang, Xu and
Wang, Bo and
Tang, Yihong and
Zhao, Dongming and
Liu, Jing and
He, Ruifang and
Hou, Yuexian",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.367/",
pages = "5475--5485",
abstract = "Empathy improves human-machine dialogue systems by enhancing the user`s experience. While traditional models have aimed to detect and express users' emotions from dialogue history, they neglect the crucial and complex interactions among emotion, emotion causes, and commonsense. To address this, we introduce the ECC (Emotion, Cause, and Commonsense) framework, which leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. ECC further employs novel loss functions to refine the interplay of three factors and generates empathetic responses using an energy-based model supported by ODE sampling. Empirical results on the EmpatheticDialogues dataset demonstrate that ECC outperforms existing baselines, offering a robust solution for empathetic dialogue generation."
}
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<abstract>Empathy improves human-machine dialogue systems by enhancing the user‘s experience. While traditional models have aimed to detect and express users’ emotions from dialogue history, they neglect the crucial and complex interactions among emotion, emotion causes, and commonsense. To address this, we introduce the ECC (Emotion, Cause, and Commonsense) framework, which leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. ECC further employs novel loss functions to refine the interplay of three factors and generates empathetic responses using an energy-based model supported by ODE sampling. Empirical results on the EmpatheticDialogues dataset demonstrate that ECC outperforms existing baselines, offering a robust solution for empathetic dialogue generation.</abstract>
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%0 Conference Proceedings
%T ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation
%A Wang, Xu
%A Wang, Bo
%A Tang, Yihong
%A Zhao, Dongming
%A Liu, Jing
%A He, Ruifang
%A Hou, Yuexian
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F wang-etal-2025-ecc
%X Empathy improves human-machine dialogue systems by enhancing the user‘s experience. While traditional models have aimed to detect and express users’ emotions from dialogue history, they neglect the crucial and complex interactions among emotion, emotion causes, and commonsense. To address this, we introduce the ECC (Emotion, Cause, and Commonsense) framework, which leverages specialized encoders to capture the key features of emotion, cause, and commonsense and collaboratively models these through a Conditional Variational Auto-Encoder. ECC further employs novel loss functions to refine the interplay of three factors and generates empathetic responses using an energy-based model supported by ODE sampling. Empirical results on the EmpatheticDialogues dataset demonstrate that ECC outperforms existing baselines, offering a robust solution for empathetic dialogue generation.
%U https://aclanthology.org/2025.coling-main.367/
%P 5475-5485
Markdown (Informal)
[ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation](https://aclanthology.org/2025.coling-main.367/) (Wang et al., COLING 2025)
ACL
- Xu Wang, Bo Wang, Yihong Tang, Dongming Zhao, Jing Liu, Ruifang He, and Yuexian Hou. 2025. ECC: Synergizing Emotion, Cause and Commonsense for Empathetic Dialogue Generation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 5475–5485, Abu Dhabi, UAE. Association for Computational Linguistics.