@inproceedings{haq-etal-2025-long,
    title = "Long-context Reference-based {MT} Quality Estimation",
    author = "Haq, Sami  and
      Osuji, Chinonso  and
      Castilho, Sheila  and
      Davis, Brian  and
      Castro Ferreira, Thiago",
    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-1.64/",
    pages = "905--912",
    ISBN = "979-8-89176-341-8",
    abstract = "In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level ESA scores using augmented long-context data. To construct long-context training examples, we concatenate multiple in-domain sentences and compute a weighted average of their scores. We further integrate human judgment datasets MQM, SQM, and DA) through score normalisation and train multilingual models on the source, hypothesis, and reference translations. Experimental results demonstrate that incorporating long-context information yields higher correlations with human judgments compared to models trained exclusively on short segments."
}