@inproceedings{chen-etal-2025-empathy,
title = "Empathy Prediction from Diverse Perspectives",
author = "Chen, Francine and
Carter, Scott and
Lau, Tatiana and
Bravo, Nayeli Suseth and
Bhattacharyya, Sumanta and
Sieck, Kate and
Wu, Charlene C.",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.439/",
doi = "10.18653/v1/2025.acl-long.439",
pages = "8959--8974",
ISBN = "979-8-89176-251-0",
abstract = "A person{'}s perspective on a topic can influence their empathy towards a story. To investigate the use of personal perspective in empathy prediction, we collected a dataset, EmpathyFromPerspectives, where a user rates their empathy towards a story by a person with a different perspective on a prompted topic. We observed in the dataset that user perspective can be important for empathy prediction and developed a model, PPEP, that uses a rater{'}s perspective as context for predicting the rater{'}s empathy towards a story. Experiments comparing PPEP with baseline models show that use of personal perspective significantly improves performance. A user study indicated that human empathy ratings of stories generally agreed with PPEP{'}s relative empathy rankings."
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<abstract>A person’s perspective on a topic can influence their empathy towards a story. To investigate the use of personal perspective in empathy prediction, we collected a dataset, EmpathyFromPerspectives, where a user rates their empathy towards a story by a person with a different perspective on a prompted topic. We observed in the dataset that user perspective can be important for empathy prediction and developed a model, PPEP, that uses a rater’s perspective as context for predicting the rater’s empathy towards a story. Experiments comparing PPEP with baseline models show that use of personal perspective significantly improves performance. A user study indicated that human empathy ratings of stories generally agreed with PPEP’s relative empathy rankings.</abstract>
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%0 Conference Proceedings
%T Empathy Prediction from Diverse Perspectives
%A Chen, Francine
%A Carter, Scott
%A Lau, Tatiana
%A Bravo, Nayeli Suseth
%A Bhattacharyya, Sumanta
%A Sieck, Kate
%A Wu, Charlene C.
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F chen-etal-2025-empathy
%X A person’s perspective on a topic can influence their empathy towards a story. To investigate the use of personal perspective in empathy prediction, we collected a dataset, EmpathyFromPerspectives, where a user rates their empathy towards a story by a person with a different perspective on a prompted topic. We observed in the dataset that user perspective can be important for empathy prediction and developed a model, PPEP, that uses a rater’s perspective as context for predicting the rater’s empathy towards a story. Experiments comparing PPEP with baseline models show that use of personal perspective significantly improves performance. A user study indicated that human empathy ratings of stories generally agreed with PPEP’s relative empathy rankings.
%R 10.18653/v1/2025.acl-long.439
%U https://aclanthology.org/2025.acl-long.439/
%U https://doi.org/10.18653/v1/2025.acl-long.439
%P 8959-8974
Markdown (Informal)
[Empathy Prediction from Diverse Perspectives](https://aclanthology.org/2025.acl-long.439/) (Chen et al., ACL 2025)
ACL
- Francine Chen, Scott Carter, Tatiana Lau, Nayeli Suseth Bravo, Sumanta Bhattacharyya, Kate Sieck, and Charlene C. Wu. 2025. Empathy Prediction from Diverse Perspectives. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8959–8974, Vienna, Austria. Association for Computational Linguistics.