@inproceedings{fu-etal-2023-reasoning,
title = "Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation",
author = "Fu, Yahui and
Inoue, Koji and
Chu, Chenhui and
Kawahara, Tatsuya",
editor = "Stoyanchev, Svetlana and
Joty, Shafiq and
Schlangen, David and
Dusek, Ondrej and
Kennington, Casey and
Alikhani, Malihe",
booktitle = "Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue",
month = sep,
year = "2023",
address = "Prague, Czechia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.sigdial-1.60",
doi = "10.18653/v1/2023.sigdial-1.60",
pages = "645--656",
abstract = "Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user{'}s experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user{'}s perspective, ignoring the system{'}s perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user{'}s perspective (user{'}s desires and reactions) and the system{'}s perspective (system{'}s intentions and reactions). We enhance ChatGPT{'}s ability to reason for the system{'}s perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.",
}
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<abstract>Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user’s experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user’s perspective, ignoring the system’s perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user’s perspective (user’s desires and reactions) and the system’s perspective (system’s intentions and reactions). We enhance ChatGPT’s ability to reason for the system’s perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.</abstract>
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%0 Conference Proceedings
%T Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation
%A Fu, Yahui
%A Inoue, Koji
%A Chu, Chenhui
%A Kawahara, Tatsuya
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F fu-etal-2023-reasoning
%X Recent approaches to empathetic response generation try to incorporate commonsense knowledge or reasoning about the causes of emotions to better understand the user’s experiences and feelings. However, these approaches mainly focus on understanding the causalities of context from the user’s perspective, ignoring the system’s perspective. In this paper, we propose a commonsense-based causality explanation approach for diverse empathetic response generation that considers both the user’s perspective (user’s desires and reactions) and the system’s perspective (system’s intentions and reactions). We enhance ChatGPT’s ability to reason for the system’s perspective by integrating in-context learning with commonsense knowledge. Then, we integrate the commonsense-based causality explanation with both ChatGPT and a T5-based model. Experimental evaluations demonstrate that our method outperforms other comparable methods on both automatic and human evaluations.
%R 10.18653/v1/2023.sigdial-1.60
%U https://aclanthology.org/2023.sigdial-1.60
%U https://doi.org/10.18653/v1/2023.sigdial-1.60
%P 645-656
Markdown (Informal)
[Reasoning before Responding: Integrating Commonsense-based Causality Explanation for Empathetic Response Generation](https://aclanthology.org/2023.sigdial-1.60) (Fu et al., SIGDIAL 2023)
ACL