@inproceedings{li-etal-2020-empdg,
title = "{E}mp{DG}: Multi-resolution Interactive Empathetic Dialogue Generation",
author = "Li, Qintong and
Chen, Hongshen and
Ren, Zhaochun and
Ren, Pengjie and
Tu, Zhaopeng and
Chen, Zhumin",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.394",
doi = "10.18653/v1/2020.coling-main.394",
pages = "4454--4466",
abstract = "A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users{'} expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model {--} EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.",
}
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<abstract>A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users’ expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model – EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.</abstract>
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%0 Conference Proceedings
%T EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation
%A Li, Qintong
%A Chen, Hongshen
%A Ren, Zhaochun
%A Ren, Pengjie
%A Tu, Zhaopeng
%A Chen, Zhumin
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F li-etal-2020-empdg
%X A humanized dialogue system is expected to generate empathetic replies, which should be sensitive to the users’ expressed emotion. The task of empathetic dialogue generation is proposed to address this problem. The essential challenges lie in accurately capturing the nuances of human emotion and considering the potential of user feedback, which are overlooked by the majority of existing work. In response to this problem, we propose a multi-resolution adversarial model – EmpDG, to generate more empathetic responses. EmpDG exploits both the coarse-grained dialogue-level and fine-grained token-level emotions, the latter of which helps to better capture the nuances of user emotion. In addition, we introduce an interactive adversarial learning framework which exploits the user feedback, to identify whether the generated responses evoke emotion perceptivity in dialogues. Experimental results show that the proposed approach significantly outperforms the state-of-the-art baselines in both content quality and emotion perceptivity.
%R 10.18653/v1/2020.coling-main.394
%U https://aclanthology.org/2020.coling-main.394
%U https://doi.org/10.18653/v1/2020.coling-main.394
%P 4454-4466
Markdown (Informal)
[EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation](https://aclanthology.org/2020.coling-main.394) (Li et al., COLING 2020)
ACL
- Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, and Zhumin Chen. 2020. EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4454–4466, Barcelona, Spain (Online). International Committee on Computational Linguistics.