Computational narrative understanding studies the identification, description, and interaction of the elements of a narrative: characters, attributes, events, and relations. Narrative research has given considerable attention to defining and classifying character types. However, these character-type taxonomies do not generalize well because they are small, too simple, or specific to a domain. We require robust and reliable benchmarks to test whether narrative models truly understand the nuances of the character's development in the story. Our work addresses this by curating the Chatter dataset that labels whether a character portrays some attribute for 88148 character-attribute pairs, encompassing 2998 characters, 13324 attributes and 660 movies. We validate a subset of Chatter, called ChatterEval, using human annotations to serve as an evaluation benchmark for the character attribution task in movie scripts. ChatterEval assesses narrative understanding and the long-context modeling capacity of language models.

本研究针对叙事理解领域中对人物与属性的识别和描述的问题，构建了CHATTER数据集，涵盖了2998个角色和13324个属性，标注了88148对角色-属性关系。这一数据集为评估叙事模型对人物发展的理解提供了可靠的基准，特别是通过ChatterEval验证其在电影剧本中的应用潜力。

CHATTER：用于叙事理解的人物归属数据集