Modeling Empathetic Alignment in Conversation

Jiamin Yang, David Jurgens


Abstract
Empathy requires perspective-taking: empathetic responses require a person to reason about what another has experienced and communicate that understanding in language. However, most NLP approaches to empathy do not explicitly model this alignment process. Here, we introduce a new approach to recognizing alignment in empathetic speech, grounded in Appraisal Theory. We introduce a new dataset of over 9.2K span-level annotations of different types of appraisals of a person’s experience and over 3K empathetic alignments between a speaker’s and observer’s speech. Through computational experiments, we show that these appraisals and alignments can be accurately recognized. In experiments in over 9.2M Reddit conversations, we find that appraisals capture meaningful groupings of behavior but that most responses have minimal alignment. However, we find that mental health professionals engage with substantially more empathetic alignment.
Anthology ID:
2024.naacl-long.172
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3127–3148
Language:
URL:
https://aclanthology.org/2024.naacl-long.172
DOI:
Bibkey:
Cite (ACL):
Jiamin Yang and David Jurgens. 2024. Modeling Empathetic Alignment in Conversation. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 3127–3148, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Modeling Empathetic Alignment in Conversation (Yang & Jurgens, NAACL 2024)
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PDF:
https://aclanthology.org/2024.naacl-long.172.pdf
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