@inproceedings{hu-etal-2025-joint,
    title = "Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction",
    author = "Hu, Jianpeng  and
      Xue, Chao  and
      Yu, Chunqing  and
      Xu, JiaCheng  and
      Tan, Chengxiang",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.findings-naacl.42/",
    pages = "714--726",
    ISBN = "979-8-89176-195-7",
    abstract = "Document-level multi-event extraction aims to identify a list of event types and corresponding arguments from the document. However, most of the current methods neglect the fine-grained difference among events in multi-event documents, which leads to event confusion and missing. This is also one of the reasons why the recall and F1-score of multi-event recognition are lower compared to single-event recognition. In this paper, we propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library, which uses a novel probe-label alignment method for differential optimization. In addition, the role contrastive loss and probe consistent loss are designed to fine-tune the fine-grained role differences and probe differences in each event. The experimental results on two general datasets show that our method outperforms the state-of-the-art method in the F1-score, especially in the recall of multi-events."
}