@inproceedings{zufle-etal-2025-comet,
    title = "{COMET}-poly: Machine Translation Metric Grounded in Other Candidates",
    author = {Z{\"u}fle, Maike  and
      Zouhar, Vil{\'e}m  and
      Dinh, Tu Anh  and
      Maia Polo, Felipe  and
      Niehues, Jan  and
      Sachan, Mrinmaya},
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Tenth Conference on Machine Translation",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.wmt-1.63/",
    pages = "887--904",
    ISBN = "979-8-89176-341-8",
    abstract = "Automated metrics for machine translation attempt to replicate human judgment. Unlike humans, who often assess a translation in the context of multiple alternatives, these metrics typically consider only the source sentence and a single translation. This discrepancy in the evaluation setup may negatively impact the performance of automated metrics. We propose two automated metrics that incorporate additional information beyond the single translation. COMET-polycand uses alternative translations of the same source sentence to compare and contrast with the translation at hand, thereby providing a more informed assessment of its quality. COMET-polyic, inspired by retrieval-based in-context learning, takes in translations of similar source texts along with their human-labeled quality scores to guide the evaluation. We find that including a single additional translation in COMET-polycand improves the segment-level metric performance (0.079 to 0.118 Kendall{'}s tau-b correlation), with further gains when more translations are added. Incorporating retrieved examples in COMET-polyic yields similar improvements (0.079 to 0.116 Kendall{'}s tau-b correlation). We release our models publicly."
}