@inproceedings{wang-etal-2026-reg,
title = "{REG}: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation",
author = "Wang, Xu and
Wang, Bo and
Xiang, Yang and
Tang, Yihong and
Zhao, Dongming and
Yuzifei and
Hou, Yuexian",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.860/",
pages = "18872--18883",
ISBN = "979-8-89176-390-6",
abstract = "Empathy relies on the cognitive capacity to relate to similar past experiences. Consequently, retrieval-based approaches utilize analogous exemplars to guide empathetic dialogue generation. However, existing methods prioritize semantic similarity over emotion characteristics, often leading to unempathetic responses. To address this, we propose REG, a framework that integrates four Emotion Attributes into the retrieval process to ensure explicit emotional alignment. Furthermore, to mitigate the noise and limited diversity caused by coarse-grained sentence-level attributes, we incorporate Token-level Retrieval for finer granularity and a Retrieval Candidate Augmentation strategy to enhance diversity. Empirical results on the EmpatheticDialogues dataset demonstrate that REG significantly outperforms baselines, offering a robust solution for empathetic generation."
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%0 Conference Proceedings
%T REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation
%A Wang, Xu
%A Wang, Bo
%A Xiang, Yang
%A Tang, Yihong
%A Zhao, Dongming
%A Hou, Yuexian
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%A Yuzifei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-reg
%X Empathy relies on the cognitive capacity to relate to similar past experiences. Consequently, retrieval-based approaches utilize analogous exemplars to guide empathetic dialogue generation. However, existing methods prioritize semantic similarity over emotion characteristics, often leading to unempathetic responses. To address this, we propose REG, a framework that integrates four Emotion Attributes into the retrieval process to ensure explicit emotional alignment. Furthermore, to mitigate the noise and limited diversity caused by coarse-grained sentence-level attributes, we incorporate Token-level Retrieval for finer granularity and a Retrieval Candidate Augmentation strategy to enhance diversity. Empirical results on the EmpatheticDialogues dataset demonstrate that REG significantly outperforms baselines, offering a robust solution for empathetic generation.
%U https://aclanthology.org/2026.acl-long.860/
%P 18872-18883
Markdown (Informal)
[REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation](https://aclanthology.org/2026.acl-long.860/) (Wang et al., ACL 2026)
ACL
- Xu Wang, Bo Wang, Yang Xiang, Yihong Tang, Dongming Zhao, Yuzifei, and Yuexian Hou. 2026. REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 18872–18883, San Diego, California, United States. Association for Computational Linguistics.