Abstract
As with many text generation tasks, the focus of recent progress on story generation has been in producing texts that are perceived to “make sense” as a whole. There are few automated metrics that address this dimension of story quality even on a shallow lexical level. To initiate investigation into such metrics, we apply a simple approach to identifying word relations that contribute to the ‘narrative sense’ of a story. We use this approach to comparatively analyze the output of a few notable story generation systems in terms of these relations. We characterize differences in the distributions of relations according to their strength within each story.- Anthology ID:
- W19-2406
- Volume:
- Proceedings of the First Workshop on Narrative Understanding
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- David Bamman, Snigdha Chaturvedi, Elizabeth Clark, Madalina Fiterau, Mohit Iyyer
- Venue:
- WNU
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44–52
- Language:
- URL:
- https://aclanthology.org/W19-2406
- DOI:
- 10.18653/v1/W19-2406
- Cite (ACL):
- Melissa Roemmele. 2019. Identifying Sensible Lexical Relations in Generated Stories. In Proceedings of the First Workshop on Narrative Understanding, pages 44–52, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Identifying Sensible Lexical Relations in Generated Stories (Roemmele, WNU 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W19-2406.pdf
- Data
- VIST, WritingPrompts