@inproceedings{yang-etal-2026-glossmate,
    title = "{G}loss{MATE}: Multi-Agent Translator Explanations for Glosses",
    author = "Yang, Changbing  and
      Littell, Patrick  and
      Bernier-Colborne, Gabriel  and
      Lu, Yanfei  and
      Geng, Mengzhe",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.lrec-1.709/",
    doi = "10.63317/5bhwdv5gcjqz",
    pages = "9018--9029",
    abstract = "This paper introduces GlossMATE, a multi-agent critique-and-judge system that translates the gloss line in Interlinear Glossed Text (IGT) into fluent English using Large Language Models (LLMs). GlossMATE integrates linguist-provided resources (e.g., gloss-tag explanations, lexicon entries, curated IGT) with in-context learning and a multi-agent critique-and-judge procedure that iteratively evaluates and refines candidate translations. Our experiments show that leveraging analogous examples, explicit linguistic explanations, and collaborative agent interactions can enhance translation quality across several low-resource and polysynthetic languages. We also incorporate human linguists into the critique loop for selected languages. Case studies on three Indigenous languages further demonstrate the complementary strengths of human-in-the-loop feedback and multi-agent reasoning for language documentation tasks."
}