@inproceedings{yang-etal-2023-exploiting-emotion,
title = "Exploiting Emotion-Semantic Correlations for Empathetic Response Generation",
author = "Yang, Zhou and
Ren, Zhaochun and
Yufeng, Wang and
Zhu, Xiaofei and
Chen, Zhihao and
Cai, Tiecheng and
Yunbing, Wu and
Su, Yisong and
Ju, Sibo and
Liao, Xiangwen",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.320",
doi = "10.18653/v1/2023.findings-emnlp.320",
pages = "4826--4837",
abstract = "Empathetic response generation aims to generate empathetic responses by understanding the speaker{'}s emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. However, linguistic research has shown that emotional words in language are dynamic and have correlations with other grammar semantic roles, i.e., words with semantic meanings, in grammar. Previous methods overlook these two characteristics, which easily lead to misunderstandings of emotions and neglect of key semantics. To address this issue, we propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM constructs dynamic emotion-semantic vectors through the interaction of context and emotions. We introduce dependency trees to reflect the correlations between emotions and semantics. Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM understands semantics and emotions more accurately and expresses fluent and informative empathetic responses. Our analysis results also indicate that the correlations between emotions and semantics are frequently used in dialogues, which is of great significance for empathetic perception and expression.",
}
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<abstract>Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. However, linguistic research has shown that emotional words in language are dynamic and have correlations with other grammar semantic roles, i.e., words with semantic meanings, in grammar. Previous methods overlook these two characteristics, which easily lead to misunderstandings of emotions and neglect of key semantics. To address this issue, we propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM constructs dynamic emotion-semantic vectors through the interaction of context and emotions. We introduce dependency trees to reflect the correlations between emotions and semantics. Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM understands semantics and emotions more accurately and expresses fluent and informative empathetic responses. Our analysis results also indicate that the correlations between emotions and semantics are frequently used in dialogues, which is of great significance for empathetic perception and expression.</abstract>
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%0 Conference Proceedings
%T Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
%A Yang, Zhou
%A Ren, Zhaochun
%A Yufeng, Wang
%A Zhu, Xiaofei
%A Chen, Zhihao
%A Cai, Tiecheng
%A Yunbing, Wu
%A Su, Yisong
%A Ju, Sibo
%A Liao, Xiangwen
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yang-etal-2023-exploiting-emotion
%X Empathetic response generation aims to generate empathetic responses by understanding the speaker’s emotional feelings from the language of dialogue. Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions. However, linguistic research has shown that emotional words in language are dynamic and have correlations with other grammar semantic roles, i.e., words with semantic meanings, in grammar. Previous methods overlook these two characteristics, which easily lead to misunderstandings of emotions and neglect of key semantics. To address this issue, we propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks. ESCM constructs dynamic emotion-semantic vectors through the interaction of context and emotions. We introduce dependency trees to reflect the correlations between emotions and semantics. Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses. Experimental results on the EMPATHETIC-DIALOGUES dataset show that ESCM understands semantics and emotions more accurately and expresses fluent and informative empathetic responses. Our analysis results also indicate that the correlations between emotions and semantics are frequently used in dialogues, which is of great significance for empathetic perception and expression.
%R 10.18653/v1/2023.findings-emnlp.320
%U https://aclanthology.org/2023.findings-emnlp.320
%U https://doi.org/10.18653/v1/2023.findings-emnlp.320
%P 4826-4837
Markdown (Informal)
[Exploiting Emotion-Semantic Correlations for Empathetic Response Generation](https://aclanthology.org/2023.findings-emnlp.320) (Yang et al., Findings 2023)
ACL
- Zhou Yang, Zhaochun Ren, Wang Yufeng, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Wu Yunbing, Yisong Su, Sibo Ju, and Xiangwen Liao. 2023. Exploiting Emotion-Semantic Correlations for Empathetic Response Generation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4826–4837, Singapore. Association for Computational Linguistics.