Personality Understanding of Fictional Characters during Book Reading
- URL: http://arxiv.org/abs/2305.10156v3
- Date: Sun, 29 Oct 2023 08:57:47 GMT
- Title: Personality Understanding of Fictional Characters during Book Reading
- Authors: Mo Yu, Jiangnan Li, Shunyu Yao, Wenjie Pang, Xiaochen Zhou, Zhou Xiao,
Fandong Meng and Jie Zhou
- Abstract summary: 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.
- Score: 81.68515671674301
- License: http://creativecommons.org/licenses/by/4.0/
- 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. The dataset is available at
https://github.com/Gorov/personet_acl23.
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