@inproceedings{maharjan-shrestha-2025-rankedcomet,
    title = "{R}anked{COMET}: Elevating a 2022 Baseline to a Top-5 Finish in the {WMT} 2025 {QE} Task",
    author = "Maharjan, Sujal  and
      Shrestha, Astha",
    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-1.74/",
    pages = "994--998",
    ISBN = "979-8-89176-341-8",
    abstract = "This paper presents rankedCOMET, a lightweight per-language-pair calibration applied to the publicly available Unbabel/wmt22-comet-da model that yields a competitive Quality Estimation (QE) system for the WMT 2025 shared task. This approach transforms raw model outputs into per-language average ranks and min{--}max normalizes those ranks to [0,1], maintaining intra-language ordering while generating consistent numeric ranges across language pairs. Applied to 742,740 test segments and submitted to Codabench, this unsupervised post-processing enhanced the aggregated Pearson correlation on the preliminary snapshot and led to a 5th-place finish. We provide detailed pseudocode, ablations (including a negative ensemble attempt), and a reproducible analysis pipeline providing Pearson, Spearman, and Kendall correlations with bootstrap confidence intervals."
}