@inproceedings{baruah-narayanan-2025-chatter,
    title = "{CHATTER}: A character-attribution dataset for narrative understanding",
    author = "Baruah, Sabyasachee  and
      Narayanan, Shrikanth",
    editor = "Clark, Elizabeth  and
      Lal, Yash Kumar  and
      Chaturvedi, Snigdha  and
      Iyyer, Mohit  and
      Brei, Anneliese  and
      Modi, Ashutosh  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the The 7th Workshop on Narrative Understanding",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wnu-1.11/",
    doi = "10.18653/v1/2025.wnu-1.11",
    pages = "52--63",
    ISBN = "979-8-89176-247-3",
    abstract = "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 88124 character-attribute pairs, encompassing 2998 characters, 12967 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 also assesses narrative understanding and the long-context modeling capacity of language models."
}