@inproceedings{sang-etal-2022-mbti,
title = "{MBTI} Personality Prediction for Fictional Characters Using Movie Scripts",
author = "Sang, Yisi and
Mou, Xiangyang and
Yu, Mo and
Wang, Dakuo and
Li, Jing and
Stanton, Jeffrey",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.500",
doi = "10.18653/v1/2022.findings-emnlp.500",
pages = "6715--6724",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T MBTI Personality Prediction for Fictional Characters Using Movie Scripts
%A Sang, Yisi
%A Mou, Xiangyang
%A Yu, Mo
%A Wang, Dakuo
%A Li, Jing
%A Stanton, Jeffrey
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F sang-etal-2022-mbti
%X 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.
%R 10.18653/v1/2022.findings-emnlp.500
%U https://aclanthology.org/2022.findings-emnlp.500
%U https://doi.org/10.18653/v1/2022.findings-emnlp.500
%P 6715-6724
Markdown (Informal)
[MBTI Personality Prediction for Fictional Characters Using Movie Scripts](https://aclanthology.org/2022.findings-emnlp.500) (Sang et al., Findings 2022)
ACL