@inproceedings{svikhnushina-etal-2022-taxonomy,
title = "A Taxonomy of Empathetic Questions in Social Dialogs",
author = "Svikhnushina, Ekaterina and
Voinea, Iuliana and
Welivita, Anuradha and
Pu, Pearl",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.211",
doi = "10.18653/v1/2022.acl-long.211",
pages = "2952--2973",
abstract = "Effective question-asking is a crucial component of a successful conversational chatbot. It could help the bots manifest empathy and render the interaction more engaging by demonstrating attention to the speaker{'}s emotions. However, current dialog generation approaches do not model this subtle emotion regulation technique due to the lack of a taxonomy of questions and their purpose in social chitchat. To address this gap, we have developed an empathetic question taxonomy (EQT), with special attention paid to questions{'} ability to capture communicative acts and their emotion-regulation intents. We further design a crowd-sourcing task to annotate a large subset of the EmpatheticDialogues dataset with the established labels. We use the crowd-annotated data to develop automatic labeling tools and produce labels for the whole dataset. Finally, we employ information visualization techniques to summarize co-occurrences of question acts and intents and their role in regulating interlocutor{'}s emotion. These results reveal important question-asking strategies in social dialogs. The EQT classification scheme can facilitate computational analysis of questions in datasets. More importantly, it can inform future efforts in empathetic question generation using neural or hybrid methods.",
}
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<abstract>Effective question-asking is a crucial component of a successful conversational chatbot. It could help the bots manifest empathy and render the interaction more engaging by demonstrating attention to the speaker’s emotions. However, current dialog generation approaches do not model this subtle emotion regulation technique due to the lack of a taxonomy of questions and their purpose in social chitchat. To address this gap, we have developed an empathetic question taxonomy (EQT), with special attention paid to questions’ ability to capture communicative acts and their emotion-regulation intents. We further design a crowd-sourcing task to annotate a large subset of the EmpatheticDialogues dataset with the established labels. We use the crowd-annotated data to develop automatic labeling tools and produce labels for the whole dataset. Finally, we employ information visualization techniques to summarize co-occurrences of question acts and intents and their role in regulating interlocutor’s emotion. These results reveal important question-asking strategies in social dialogs. The EQT classification scheme can facilitate computational analysis of questions in datasets. More importantly, it can inform future efforts in empathetic question generation using neural or hybrid methods.</abstract>
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%0 Conference Proceedings
%T A Taxonomy of Empathetic Questions in Social Dialogs
%A Svikhnushina, Ekaterina
%A Voinea, Iuliana
%A Welivita, Anuradha
%A Pu, Pearl
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F svikhnushina-etal-2022-taxonomy
%X Effective question-asking is a crucial component of a successful conversational chatbot. It could help the bots manifest empathy and render the interaction more engaging by demonstrating attention to the speaker’s emotions. However, current dialog generation approaches do not model this subtle emotion regulation technique due to the lack of a taxonomy of questions and their purpose in social chitchat. To address this gap, we have developed an empathetic question taxonomy (EQT), with special attention paid to questions’ ability to capture communicative acts and their emotion-regulation intents. We further design a crowd-sourcing task to annotate a large subset of the EmpatheticDialogues dataset with the established labels. We use the crowd-annotated data to develop automatic labeling tools and produce labels for the whole dataset. Finally, we employ information visualization techniques to summarize co-occurrences of question acts and intents and their role in regulating interlocutor’s emotion. These results reveal important question-asking strategies in social dialogs. The EQT classification scheme can facilitate computational analysis of questions in datasets. More importantly, it can inform future efforts in empathetic question generation using neural or hybrid methods.
%R 10.18653/v1/2022.acl-long.211
%U https://aclanthology.org/2022.acl-long.211
%U https://doi.org/10.18653/v1/2022.acl-long.211
%P 2952-2973
Markdown (Informal)
[A Taxonomy of Empathetic Questions in Social Dialogs](https://aclanthology.org/2022.acl-long.211) (Svikhnushina et al., ACL 2022)
ACL
- Ekaterina Svikhnushina, Iuliana Voinea, Anuradha Welivita, and Pearl Pu. 2022. A Taxonomy of Empathetic Questions in Social Dialogs. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2952–2973, Dublin, Ireland. Association for Computational Linguistics.