@inproceedings{majumder-etal-2020-mime,
title = "{MIME}: {MIM}icking Emotions for Empathetic Response Generation",
author = "Majumder, Navonil and
Hong, Pengfei and
Peng, Shanshan and
Lu, Jiankun and
Ghosal, Deepanway and
Gelbukh, Alexander and
Mihalcea, Rada and
Poria, Soujanya",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.721",
doi = "10.18653/v1/2020.emnlp-main.721",
pages = "8968--8979",
abstract = "Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at \url{https://github.com/declare-lab/MIME}.",
}
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<abstract>Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME.</abstract>
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%0 Conference Proceedings
%T MIME: MIMicking Emotions for Empathetic Response Generation
%A Majumder, Navonil
%A Hong, Pengfei
%A Peng, Shanshan
%A Lu, Jiankun
%A Ghosal, Deepanway
%A Gelbukh, Alexander
%A Mihalcea, Rada
%A Poria, Soujanya
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F majumder-etal-2020-mime
%X Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations. The implementation of MIME is publicly available at https://github.com/declare-lab/MIME.
%R 10.18653/v1/2020.emnlp-main.721
%U https://aclanthology.org/2020.emnlp-main.721
%U https://doi.org/10.18653/v1/2020.emnlp-main.721
%P 8968-8979
Markdown (Informal)
[MIME: MIMicking Emotions for Empathetic Response Generation](https://aclanthology.org/2020.emnlp-main.721) (Majumder et al., EMNLP 2020)
ACL
- Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, and Soujanya Poria. 2020. MIME: MIMicking Emotions for Empathetic Response Generation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8968–8979, Online. Association for Computational Linguistics.