Abstract
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.
Original language | English (US) |
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Pages | 6744-6753 |
Number of pages | 10 |
State | Published - 2022 |
Event | 2022 Findings of the Association for Computational Linguistics: EMNLP 2022 - Abu Dhabi, United Arab Emirates Duration: Dec 7 2022 → Dec 11 2022 |
Conference
Conference | 2022 Findings of the Association for Computational Linguistics: EMNLP 2022 |
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Country/Territory | United Arab Emirates |
City | Abu Dhabi |
Period | 12/7/22 → 12/11/22 |
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Computer Science Applications
- Information Systems