@inproceedings{stammbach-etal-2022-heroes,
    title = "Heroes, Villains, and Victims, and {GPT}-3: Automated Extraction of Character Roles Without Training Data",
    author = "Stammbach, Dominik  and
      Antoniak, Maria  and
      Ash, Elliott",
    editor = "Clark, Elizabeth  and
      Brahman, Faeze  and
      Iyyer, Mohit",
    booktitle = "Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2022.wnu-1.6/",
    doi = "10.18653/v1/2022.wnu-1.6",
    pages = "47--56",
    abstract = "This paper shows how to use large-scale pretrained language models to extract character roles from narrative texts without domain-specific training data. Queried with a zero-shot question-answering prompt, GPT-3 can identify the hero, villain, and victim in diverse domains: newspaper articles, movie plot summaries, and political speeches."
}