Abstract
We present a novel approach to modeling fictional narratives. The proposed model creates embeddings that represent a story such that similar narratives, that is, reformulations of the same story, will result in similar embeddings. We showcase the prowess of our narrative-focused embeddings on various datasets, exhibiting state-of-the-art performance on multiple retrieval tasks. The embeddings also show promising results on a narrative understanding task. Additionally, we perform an annotation-based evaluation to validate that our introduced computational notion of narrative similarity aligns with human perception. The approach can help to explore vast datasets of stories, with potential applications in recommender systems and in the computational analysis of literature.- Anthology ID:
- 2024.emnlp-main.339
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5931–5943
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.339/
- DOI:
- 10.18653/v1/2024.emnlp-main.339
- Cite (ACL):
- Hans Ole Hatzel and Chris Biemann. 2024. Story Embeddings — Narrative-Focused Representations of Fictional Stories. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5931–5943, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Story Embeddings — Narrative-Focused Representations of Fictional Stories (Hatzel & Biemann, EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.emnlp-main.339.pdf