Identifying Sensible Lexical Relations in Generated Stories

Melissa Roemmele


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/W19-2406.pdf
Data
VISTWritingPrompts