@inproceedings{phuangrot-etal-2026-machine,
    title = "Machine translation Evaluation {E}ng-{T}hai {MQM} Ranking dataset",
    author = "Phuangrot, Phichet  and
      Trintawat, Natdanai  and
      Vilasri, Kanawat  and
      Patcharawiwatpong, Yanapat  and
      Boonsarngsuk, Pachara  and
      Pavasant, Nat  and
      Chuangsuwanich, Ekapol",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 2: Short Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-short.41/",
    pages = "572--587",
    ISBN = "979-8-89176-381-4",
    abstract = "We introduce MEET-MR (Machine Translation English{--}Thai MQM and Ranking Dataset), a comprehensive benchmark for evaluating English{--}Thai machine translation systems. The dataset is constructed using the Multidimensional Quality Metrics (MQM) annotation framework, providing fine-grained human judgements of translation quality. In addition, MEET-MR includes human preference rankings and reference translations, enabling both absolute and relative assessment of translation quality. The dataset covers nine diverse domains providing linguistic and contextual diversity. By combining high-quality reference translations, objective MQM error annotations, and subjective preference rankings, MEET-MR serves as a valuable resource for studying translation quality estimation, model alignment with human evaluation, and cross-domain performance in English{--}Thai machine translation. MEET-MR is publicly available at https://huggingface.co/datasets/Chula-AI/MEET-MR"
}