@inproceedings{dey-girju-2022-enriching,
    title = "Enriching Deep Learning with Frame Semantics for Empathy Classification in Medical Narrative Essays",
    author = "Dey, Priyanka  and
      Girju, Roxana",
    editor = "Lavelli, Alberto  and
      Holderness, Eben  and
      Jimeno Yepes, Antonio  and
      Minard, Anne-Lyse  and
      Pustejovsky, James  and
      Rinaldi, Fabio",
    booktitle = "Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.louhi-1.23/",
    doi = "10.18653/v1/2022.louhi-1.23",
    pages = "207--217",
    abstract = "Empathy is a vital component of health care and plays a key role in the training of future doctors. Paying attention to medical students' self-reflective stories of their interactions with patients can encourage empathy and the formation of professional identities that embody desirable values such as integrity and respect. We present a computational approach and linguistic analysis of empathic language in a large corpus of 440 essays written by pre-med students as narrated simulated patient {--} doctor interactions. We analyze the discourse of three kinds of empathy: cognitive, affective, and prosocial as highlighted by expert annotators. We also present various experiments with state-of-the-art recurrent neural networks and transformer models for classifying these forms of empathy. To further improve over these results, we develop a novel system architecture that makes use of frame semantics to enrich our state-of-the-art models. We show that this novel framework leads to significant improvement on the empathy classification task for this dataset."
}