@inproceedings{ding-etal-2025-fusion,
    title = "Fusion meets Function: The Adaptive Selection-Generation Approach in Event Argument Extraction",
    author = "Ding, Guoxuan  and
      Guo, Xiaobo  and
      Wang, Xin  and
      Wang, Lei  and
      Fu, Tianshu  and
      Mu, Nan  and
      Zha, Daren",
    editor = "Rambow, Owen  and
      Wanner, Leo  and
      Apidianaki, Marianna  and
      Al-Khalifa, Hend  and
      Eugenio, Barbara Di  and
      Schockaert, Steven",
    booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.coling-main.294/",
    pages = "4359--4369",
    abstract = "Event Argument Extraction is a critical task of Event Extraction, focused on identifying event arguments within text. This paper presents a novel Fusion Selection-Generation-Based Approach, by combining the precision of selective methods with the semantic generation capability of generative methods to enhance argument extraction accuracy. This synergistic integration, achieved through fusion prompt, element-based extraction, and fusion learning, addresses the challenges of input, process, and output fusion, effectively blending the unique characteristics of both methods into a cohesive model. Comprehensive evaluations on the RAMS and WikiEvents demonstrate the model`s state-of-the-art performance and efficiency."
}