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.- Anthology ID:
- 2022.wnu-1.6
- Volume:
- Proceedings of the 4th Workshop of Narrative Understanding (WNU2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Elizabeth Clark, Faeze Brahman, Mohit Iyyer
- Venue:
- WNU
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 47–56
- Language:
- URL:
- https://aclanthology.org/2022.wnu-1.6
- DOI:
- 10.18653/v1/2022.wnu-1.6
- Cite (ACL):
- Dominik Stammbach, Maria Antoniak, and Elliott Ash. 2022. Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data. In Proceedings of the 4th Workshop of Narrative Understanding (WNU2022), pages 47–56, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data (Stammbach et al., WNU 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.wnu-1.6.pdf