@inproceedings{ravikumar-batista-navarro-2026-tasks,
    title = "When Tasks Share Structure: A Comparative Study of Training Strategies for Generative Event Extraction",
    author = "Ravikumar, Rishi  and
      Batista-Navarro, Riza",
    editor = {H{\"u}rriyeto{\u{g}}lu, Ali  and
      Thapa, Surendrabikram  and
      Tanev, Hristo},
    booktitle = "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications ({EEUCA} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.eeuca-1.5/",
    pages = "38--48",
    ISBN = "979-8-89176-402-6",
    abstract = "Event extraction requires performing two interdependent subtasks: event detection and event argument extraction. While prior work has explored pipelined and joint training approaches, the question of how best to coordinate training across these subtasks in generative LLM-based systems remains open. We present a systematic study comparing three training paradigms: disjoint, fully shared and hybrid weight allocation, instantiated as eight concrete strategies and evaluated on ACE2005 and RichERE across multiple instruction-tuned LLMs. Our findings show that training strategy has a consistent and meaningful effect on extraction accuracy, and that a clear best-performing strategy emerges across models and benchmarks. We believe that these findings could extend beyond event extraction to other information extraction tasks that decompose into interdependent subtasks."
}