@InProceedings{alotaibi:2026:wmt,
  author    = {Alotaibi, Abrar M.},
  title     = {OpenBracket at WMT 2026: COMET-MBR System Combination over Arabic-Native and Multilingual Models for Low-Resource Arabic-English Translation},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2273--2279},
  abstract  = {OpenBracket submits constrained systems to both English-paired directions of the WMT 2026 Low-Resource Arabic-Asian Language Translation task, trained on the official data only (21,000 pairs) from publicly available checkpoints. Per direction we submit a primary COMET-22 MBR system combination over five fine-tuned models (NLLB-3.3B, two X-ALMA variants, ALLaM-7B, OPUS-MT) plus two contrastive systems: an ALLaM-7B fine-tune and sampling-based MBR over NLLB-3.3B. On the official test set, our primary ranks first among all teams in Arabic-to-English (BLEU 33.83, chrF 59.72, TER 57.00, COMET 82.49), and in English-to-Arabic it attains the highest COMET on the primary leaderboard (BLEU 21.81, chrF 54.99, TER 69.29, COMET 85.23).},
  url       = {https://aclanthology.org/2026.wmt-1.167}
}