@inproceedings{man-etal-2026-multi,
    title = "Can Multi-agent Help Disambiguation in Multi-domain Translation?",
    author = "Man, Zhibo  and
      Xu, Shaoyang  and
      Zhang, Yujie  and
      Feng, Yi  and
      Chen, Yuanmeng  and
      Chen, Yufeng  and
      Jinan, Xu  and
      Zhang, Wenxuan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.907/",
    pages = "18234--18244",
    ISBN = "979-8-89176-395-1",
    abstract = "Large language models (LLMs)-based multi-agent systems have recently shown strong potential for machine translation (MT). However, their application to multi-domain translation (MDT) remains under-explored, particularly in addressing cross-domain word ambiguity. To investigate whether multi-agent approaches can help disambiguation in MDT, we propose a multi-agent collaborative disambiguation framework for MDT (MACD), which leverages the collaborative capabilities of LLMs for disambiguation. MACD consists of four cooperating agents responsible for domain allocation, general translation, domain disambiguation, and translation fusion. Experimental results show that MACD significantly improves translation performance across multiple domains and enhances disambiguation accuracy. Our approach reveals several findings on multi-agent collaboration in resolving word ambiguities."
}