  An NLP model that understands stories should be able to understand the
characters in them. To support the development of neural models for this
purpose, we construct a benchmark, Story2Personality. The task is to predict a
movie character's MBTI or Big 5 personality types based on the narratives of
the character. Experiments show that our task is challenging for the existing
text classification models, as none is able to largely outperform random
guesses. We further proposed a multi-view model for personality prediction
using both verbal and non-verbal descriptions, which gives improvement compared
to using only verbal descriptions. The uniqueness and challenges in our dataset
call for the development of narrative comprehension techniques from the
perspective of understanding characters.
