Modeling Naive Psychology of Characters in Simple Commonsense Stories

Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, Yejin Choi


Abstract
Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people’s mental states — a capability that is trivial for humans but remarkably hard for machines. To facilitate research addressing this challenge, we introduce a new annotation framework to explain naive psychology of story characters as fully-specified chains of mental states with respect to motivations and emotional reactions. Our work presents a new large-scale dataset with rich low-level annotations and establishes baseline performance on several new tasks, suggesting avenues for future research.
Anthology ID:
P18-1213
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2289–2299
Language:
URL:
https://aclanthology.org/P18-1213
DOI:
10.18653/v1/P18-1213
Bibkey:
Cite (ACL):
Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, and Yejin Choi. 2018. Modeling Naive Psychology of Characters in Simple Commonsense Stories. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2289–2299, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Modeling Naive Psychology of Characters in Simple Commonsense Stories (Rashkin et al., ACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/update-css-js/P18-1213.pdf
Note:
 P18-1213.Notes.pdf
Presentation:
 P18-1213.Presentation.pdf
Video:
 https://vimeo.com/285805584
Data
Story CommonsenseROCStories