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://www.aclweb.org/anthology/P18-1213
DOI:
10.18653/v1/P18-1213
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/P18-1213.pdf
Note:
 P18-1213.Notes.pdf
Video:
 https://vimeo.com/285805584
Presentation:
 P18-1213.Presentation.pdf