Abstract
Characters are integral to long-form narratives, but are poorly understood by existing story analysis and generation systems. While prior work has simplified characters via graph-based methods and brief character descriptions, we aim to better tackle the problem of representing complex characters by taking inspiration from advice given to professional writers. We propose CHIRON, a new ‘character sheet’ based representation that organizes and filters textual information about characters. We construct CHIRON sheets in two steps: a Generation Module that prompts an LLM for character information via question-answering and a Validation Module that uses automated reasoning and a domain-specific entailment model to eliminate false facts about a character. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. We also show that metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.- Anthology ID:
- 2024.findings-emnlp.499
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8523–8547
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.499/
- DOI:
- 10.18653/v1/2024.findings-emnlp.499
- Cite (ACL):
- Alexander Gurung and Mirella Lapata. 2024. CHIRON: Rich Character Representations in Long-Form Narratives. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 8523–8547, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- CHIRON: Rich Character Representations in Long-Form Narratives (Gurung & Lapata, Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.499.pdf