Modeling Naive Psychology of Characters in Simple Commonsense Stories
This work addresses the problem of narrative comprehension for AI systems, though it is incremental as it focuses on dataset creation and baseline establishment.
The paper tackles the challenge of machines understanding narrative implications and character mental states by introducing a new annotation framework for naive psychology, resulting in a large-scale dataset and baseline performance on new tasks.
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.