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
this https URL

这篇论文介绍了一个名为 PersoNet 的数据集，该数据集是研究角色个性的第一个标记数据集，并且通过在线阅读应用程序中用户笔记的注释策略进行了标注。实验和人类研究表明，我们的数据集构建既高效又准确，并且我们的任务需要长期的上下文才能为机器和人类获得准确的预测结果。

阅读小说时虚构人物性格理解

Personality Understanding of Fictional Characters during Book Reading

Recently, the long short-term memory neural network (LSTM) has attracted wide
interest due to its success in many tasks. LSTM architecture consists of a
memory cell and three gates, which looks similar to the neuronal networks in
the brain. However, there still lacks the evidence of the cognitive
plausibility of LSTM architecture as well as its working mechanism. In this
paper, we study the cognitive plausibility of LSTM by aligning its internal
architecture with the brain activity observed via fMRI when the subjects read a
story. Experiment results show that the artificial memory vector in LSTM can
accurately predict the observed sequential brain activities, indicating the
correlation between LSTM architecture and the cognitive process of story
reading.

本文研究了长短时记忆神经网络（LSTM）的认知合理性，并通过将其内部架构与读故事时观察到的脑活动进行对齐来验证其工作机制，实验结果表明 LSTM 人工记忆向量可以精确预测连续性的脑活动，揭示了 LSTM 架构和阅读故事的认知过程之间的相关性。