@inproceedings{yu-etal-2023-personality,
title = "Personality Understanding of Fictional Characters during Book Reading",
author = "Yu, Mo and
Li, Jiangnan and
Yao, Shunyu and
Pang, Wenjie and
Zhou, Xiaochen and
Xiao, Zhou and
Meng, Fandong and
Zhou, Jie",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.826",
doi = "10.18653/v1/2023.acl-long.826",
pages = "14784--14802",
abstract = "Comprehending characters{'} personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived. This leads to a natural problem of situated and fine-grained personality understanding. The problem has not been studied in the NLP field, primarily due to the lack of appropriate datasets mimicking the process of book reading. We present the first labeled dataset PersoNet for this problem. Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books. Experiments and human studies indicate that our dataset construction is both efficient and accurate; and our task heavily relies on long-term context to achieve accurate predictions for both machines and humans.",
}
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<abstract>Comprehending characters’ personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived. This leads to a natural problem of situated and fine-grained personality understanding. The problem has not been studied in the NLP field, primarily due to the lack of appropriate datasets mimicking the process of book reading. We present the first labeled dataset PersoNet for this problem. Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books. Experiments and human studies indicate that our dataset construction is both efficient and accurate; and our task heavily relies on long-term context to achieve accurate predictions for both machines and humans.</abstract>
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%0 Conference Proceedings
%T Personality Understanding of Fictional Characters during Book Reading
%A Yu, Mo
%A Li, Jiangnan
%A Yao, Shunyu
%A Pang, Wenjie
%A Zhou, Xiaochen
%A Xiao, Zhou
%A Meng, Fandong
%A Zhou, Jie
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yu-etal-2023-personality
%X Comprehending characters’ personalities is a crucial aspect of story reading. As readers engage with a story, their understanding of a character evolves based on new events and information; and multiple fine-grained aspects of personalities can be perceived. This leads to a natural problem of situated and fine-grained personality understanding. The problem has not been studied in the NLP field, primarily due to the lack of appropriate datasets mimicking the process of book reading. We present the first labeled dataset PersoNet for this problem. Our novel annotation strategy involves annotating user notes from online reading apps as a proxy for the original books. Experiments and human studies indicate that our dataset construction is both efficient and accurate; and our task heavily relies on long-term context to achieve accurate predictions for both machines and humans.
%R 10.18653/v1/2023.acl-long.826
%U https://aclanthology.org/2023.acl-long.826
%U https://doi.org/10.18653/v1/2023.acl-long.826
%P 14784-14802
Markdown (Informal)
[Personality Understanding of Fictional Characters during Book Reading](https://aclanthology.org/2023.acl-long.826) (Yu et al., ACL 2023)
ACL
- Mo Yu, Jiangnan Li, Shunyu Yao, Wenjie Pang, Xiaochen Zhou, Zhou Xiao, Fandong Meng, and Jie Zhou. 2023. Personality Understanding of Fictional Characters during Book Reading. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 14784–14802, Toronto, Canada. Association for Computational Linguistics.