阅读书籍过程中对虚构角色的人格理解
Personality Understanding of Fictional Characters during Book Reading
AI总结：
本文针对阅读中情境化且细粒度的人格理解问题，提出首个标注数据集 PersoNet，通过标注在线用户笔记构建数据，并验证了长期上下文对准确预测的关键作用。
摘要：
理解角色的人格是故事阅读的一个关键方面。当读者沉浸在故事中时，他们对角色的理解会随着新事件和新信息而不断演变；并且可以感知到人格的多个细粒度方面。这引出了一个自然的问题：情境化且细粒度的人格理解。该问题在自然语言处理领域尚未被研究，主要原因是缺乏模拟书籍阅读过程的合适数据集。我们提出了首个用于该问题的标注数据集 PersoNet。我们新颖的标注策略涉及将在线阅读应用中的用户笔记标注为原始书籍的代理。实验和人工研究表明，我们的数据集构建既高效又准确；并且我们的任务在很大程度上依赖长期上下文，才能使机器和人类都实现准确的预测。该数据集可在 https://github.com/Gorov/personet_acl23 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
