@inproceedings{kim-etal-2021-perspective,
title = "Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes",
author = "Kim, Hyunwoo and
Kim, Byeongchang and
Kim, Gunhee",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.170",
doi = "10.18653/v1/2021.emnlp-main.170",
pages = "2227--2240",
abstract = "Empathy is a complex cognitive ability based on the reasoning of others{'} affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other{'}s emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.",
}
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<abstract>Empathy is a complex cognitive ability based on the reasoning of others’ affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other’s emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.</abstract>
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%0 Conference Proceedings
%T Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
%A Kim, Hyunwoo
%A Kim, Byeongchang
%A Kim, Gunhee
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F kim-etal-2021-perspective
%X Empathy is a complex cognitive ability based on the reasoning of others’ affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other’s emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.
%R 10.18653/v1/2021.emnlp-main.170
%U https://aclanthology.org/2021.emnlp-main.170
%U https://doi.org/10.18653/v1/2021.emnlp-main.170
%P 2227-2240
Markdown (Informal)
[Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://aclanthology.org/2021.emnlp-main.170) (Kim et al., EMNLP 2021)
ACL