@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}