Modeling Naive Psychology of Characters in Simple Commonsense Stories

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.

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Datasets


Introduced in the Paper:

Story Commonsense

Used in the Paper:

ROCStories
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Emotion Classification ROCStories NPN + Explanation Training F1 30.29 # 2

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