@inproceedings{guan-etal-2025-mmd,
    title = "{MMD}-{ERE}: Multi-Agent Multi-Sided Debate for Event Relation Extraction",
    author = "Guan, Yong  and
      Peng, Hao  and
      Hou, Lei  and
      Li, Juanzi",
    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/ingest_wac_2008/2025.coling-main.460/",
    pages = "6889--6896",
    abstract = "Event relation extraction (ERE) is becoming increasingly important in the era of large language models. An extensive body of research has explored how performance can be further enhanced by the emergence of exciting technologies like chain-of-thought and self-refinement. In this paper, we introduce MMD-ERE, a multi-agent multi-sided debate approach for event relation extraction, which explores the understanding of event relations among different participants before and after debate. Specifically, for organizing the debate, participants are divided into multiple groups, each assigned its own debate topic, and the process effectively integrates both cooperation and confrontation. We also regard the audience as a crucial participant, as their conclusions from an observer`s perspective tend to be more objective. In the end, we explore the understanding of event relations among different participants before and after the debate. Experiments across various ERE tasks and LLMs demonstrate that MMD-ERE outperforms established baselines. Further analysis shows that debates can effectively enhance participants' understanding of event relations."
}