@inproceedings{gatto-etal-2025-document,
    title = "Document-Level Event-Argument Data Augmentation for Challenging Role Types",
    author = "Gatto, Joseph  and
      Sharif, Omar  and
      Seegmiller, Parker  and
      Preum, Sarah Masud",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1221/",
    pages = "25109--25131",
    ISBN = "979-8-89176-251-0",
    abstract = "Event Argument Extraction (EAE) is a daunting information extraction problem {---} with significant limitations in few-shot cross-domain (FSCD) settings. A common solution to FSCD modeling is data augmentation. Unfortunately, existing augmentation methods are not well-suited to a variety of real-world EAE contexts, including (i) modeling long documents (documents with over 10 sentences), and (ii) modeling challenging role types (i.e., event roles with little to no training data and semantically outlying roles). We introduce two novel LLM-powered data augmentation methods for generating extractive document-level EAE samples using zero in-domain training data. We validate the generalizability of our approach on four datasets {---} showing significant performance increases in low-resource settings. Our highest performing models provide a 13-pt increase in F1 score on zero-shot role extraction in FSCD evaluation."
}