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.

Related papers

• BookWorm: A Dataset for Character Description and Analysis [59.186325346763184]
  We define two tasks: character description, which generates a brief factual profile, and character analysis, which offers an in-depth interpretation. We introduce the BookWorm dataset, pairing books from the Gutenberg Project with human-written descriptions and analyses. Our findings show that retrieval-based approaches outperform hierarchical ones in both tasks.
  arXiv  Detail & Related papers  (2024-10-14T10:55:58Z)
• Generating Visual Stories with Grounded and Coreferent Characters [63.07511918366848]
  We present the first model capable of predicting visual stories with consistently grounded and coreferent character mentions. Our model is finetuned on a new dataset which we build on top of the widely used VIST benchmark. We also propose new evaluation metrics to measure the richness of characters and coreference in stories.
  arXiv  Detail & Related papers  (2024-09-20T14:56:33Z)
• CHIRON: Rich Character Representations in Long-Form Narratives [98.273323001781]
  We propose CHIRON, a new character sheet' based representation that organizes and filters textual information about characters. We validate CHIRON via the downstream task of masked-character prediction, where our experiments show CHIRON is better and more flexible than comparable summary-based baselines. metrics derived from CHIRON can be used to automatically infer character-centricity in stories, and that these metrics align with human judgments.
  arXiv  Detail & Related papers  (2024-06-14T17:23:57Z)
• Enhancing Consistency and Role-Specific Knowledge Capturing by Rebuilding Fictional Character's Persona [6.220415006158471]
  Assistants API often fails to achieve with its search because the information extraction part is different each time. It is hard to maintain a consistent persona simply by using the persona document as input to the Assistants API. CharacterGPT is a novel persona reconstruction framework to alleviate the shortcomings of the Assistants API.
  arXiv  Detail & Related papers  (2024-05-30T07:44:16Z)
• PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection [50.66968526809069]
  We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
  arXiv  Detail & Related papers  (2023-10-31T08:23:33Z)
• Detecting and Grounding Important Characters in Visual Stories [18.870236356616907]
  We introduce the VIST-Character dataset, which provides rich character-centric annotations. Based on this dataset, we propose two new tasks: important character detection and character grounding in visual stories. We develop simple, unsupervised models based on distributional similarity and pre-trained vision-and-language models.
  arXiv  Detail & Related papers  (2023-03-30T18:24:06Z)
• MBTI Personality Prediction for Fictional Characters Using Movie Scripts [34.24896499537589]
  We construct a benchmark, Story2Personality, to predict a movie character's MBTI or Big 5 personality types. Experiments show that our task is challenging for the existing text classification models. We propose a multi-view model for personality prediction using both verbal and non-verbal descriptions.
  arXiv  Detail & Related papers  (2022-10-20T03:41:07Z)
• Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters [66.17039929803933]
  We propose a novel transfer learning framework which updates only $0.3%$ of model parameters to learn style specific attributes for response generation. We learn style specific attributes from the PERSONALITY-CAPTIONS dataset.
  arXiv  Detail & Related papers  (2022-10-07T00:09:22Z)
• "Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding [31.803481510886378]
  We present LiSCU -- a new dataset of literary pieces and their summaries paired with descriptions of characters that appear in them. We also introduce two new tasks on LiSCU: Character Identification and Character Description Generation. Our experiments with several pre-trained language models adapted for these tasks demonstrate that there is a need for better models of narrative comprehension.
  arXiv  Detail & Related papers  (2021-09-12T06:12:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.