“Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding
Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, Snigdha Chaturvedi
Abstract
When reading a literary piece, readers often make inferences about various characters’ roles, personalities, relationships, intents, actions, etc. While humans can readily draw upon their past experiences to build such a character-centric view of the narrative, understanding characters in narratives can be a challenging task for machines. To encourage research in this field of character-centric narrative understanding, we present LiSCU – a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.- Anthology ID:
- 2021.findings-emnlp.150
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1734–1752
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.150
- DOI:
- 10.18653/v1/2021.findings-emnlp.150
- Cite (ACL):
- Faeze Brahman, Meng Huang, Oyvind Tafjord, Chao Zhao, Mrinmaya Sachan, and Snigdha Chaturvedi. 2021. “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1734–1752, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- “Let Your Characters Tell Their Story”: A Dataset for Character-Centric Narrative Understanding (Brahman et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2021.findings-emnlp.150.pdf