@inproceedings{sommer-becker-2026-plays,
    title = "Who Plays Which Role? Protagonist Detection and Classification in Moral Discourse",
    author = "Sommer, Mirko  and
      Becker, Maria",
    editor = "Baez Santamaria, Selene  and
      Somayajula, Sai Ashish  and
      Yamaguchi, Atsuki",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 4: Student Research Workshop)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-srw.27/",
    pages = "375--392",
    ISBN = "979-8-89176-383-8",
    abstract = "Protagonists play a central role in moral discourse by structuring responsibility and authority, yet computational work has largely focused on moral values rather than the actors involved. We address this gap by studying phrase-level protagonist detection and classification in the Moralization Corpus (Becker et al., 2025), a dataset of moral arguments across different text genres. We decompose the task into identifying protagonist mentions and classifying them by what kind of actor they are (e.g., individual or institution) and what function they serve in the moral argument.We compare fine-tuned lightweight models, state-of-the-art NER models, and prompting-based large language models. We further establish human baselines and analyze the impact of contextual information on human and model decisions. Our results show that fine-tuned NER models achieve competitive detection performance at substantially lower cost than prompted large language models, and that role classification benefits more strongly from contextualized prompting. Across tasks, top-performing models reach or exceed human-level performance, highlighting the value of task decomposition for modeling protagonists in moral discourse.We release our code, predictions, and supplementary material in our project repository."
}