Abstract: Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious im-plications 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 per-formance on several new tasks, suggesting avenues for future research.
Authors: Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, Yejin Choi
(University of Washington, Allen Institute for Artificial Intelligence, University of Southern California)