@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.",
}

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.
Yu, Mo Li, Jiangnan Yao, Shunyu Pang, Wenjie Zhou, Xiaochen Xiao, Zhou Meng, F ong Zhou, Jie
Personality Understanding of Fictional Characters during Book Reading