Personality Understanding of Fictional Characters during Book Reading (2023)

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.

## introduction lively fictional characters with distinct personalities are the first drive of the plotline developments. the authors shape the characters with various personality types, which distinguish a character from others and explain the motivations and behaviors of the characters. as a reverse process, the readers grasp the characters' personalities during reading a story, which helps to understand the logics of a plot and predict its future developments. the nlp community has also recognized the values of personality understanding; and conducted studies @xcite @xcite along this direction. in the problem definition of the existing tasks, the input is an entire book. by construction, they ask for the general impression of character personalities. also for this reason, they only focus on coarse-grained personality types, e.g., the four coarse mbti types @xcite . ## related work story book understanding has been recognized as a challenging and rewarding direction @xcite . many evaluation benchmarks on various narrative understanding tasks have been developed, such as plot structure analysis @xcite , question answering @xcite @xcite , summarization @xcite @xcite , character identification and relationship extraction @xcite @xcite @xcite @xcite . all of the prior work takes the entire long story as input to a model for predictions. none of them considers the situated reading process like ours. existing strategies of dataset construction over long stories fall into the following categories: •a straightforward way is to have labelers read the entire stories. because of the huge efforts, it only works for short stories for young children @xcite or simpler tasks like coref @xcite , ner @xcite and quotation attribution @xcite . flekova and gurevych (2015) used fans' rated mbti types to create a classification task for book characters; ladhak et al. (2020); kryściński et al. (2021) created a book chapter summarization task based on summaries on the english learning websites; and thai et al. (2022) created a book retrieval task based on quotes in literature reviews. the drawback of this strategy is that the tasks can be supported are limited by the available resources. the limitations of these strategies make them insufficient to create datasets for our task of situated personality understanding. ## problem definition our personet is the first task on situated prediction of characters' personality traits in book contexts. that is, we aim to predict the traits reflected by a local snippet of book, given all the previous book content as the background (figure 1 ). ## models for persona prediction we design models based on two different types of pre-trained models, bert @xcite and longformer @xcite . we use the latter model to investigate the strength of models that are pre-trained to handle long contexts. ## conclusion we propose a dataset personet for the new problem of situated personality understanding of book characters. we overcome the difficulty in dataset construction with a new strategy of annotating the user notes as a proxy for the original books. our dataset constuction method maintains both efficiency and accuracy. experiments show that the task raised challenges of long-text understanding for both humans and machines.