@inproceedings{li-etal-2021-document,
    title = "Document-Level Event Argument Extraction by Conditional Generation",
    author = "Li, Sha  and
      Ji, Heng  and
      Han, Jiawei",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2021.naacl-main.69/",
    doi = "10.18653/v1/2021.naacl-main.69",
    pages = "894--908",
    abstract = "Event extraction has long been treated as a sentence-level task in the IE community. We argue that this setting does not match human informative seeking behavior and leads to incomplete and uninformative extraction results. We propose a document-level neural event argument extraction model by formulating the task as conditional generation following event templates. We also compile a new document-level event extraction benchmark dataset WikiEvents which includes complete event and coreference annotation. On the task of argument extraction, we achieve an absolute gain of 7.6{\%} F1 and 5.7{\%} F1 over the next best model on the RAMS and WikiEvents dataset respectively. On the more challenging task of informative argument extraction, which requires implicit coreference reasoning, we achieve a 9.3{\%} F1 gain over the best baseline. To demonstrate the portability of our model, we also create the first end-to-end zero-shot event extraction framework and achieve 97{\%} of fully supervised model`s trigger extraction performance and 82{\%} of the argument extraction performance given only access to 10 out of the 33 types on ACE."
}