@InProceedings{laskar-EtAl:2026:wmt,
  author    = {Laskar, Sahinur Rahman  and  Alam, Firoj  and  Paul, Bishwaraj  and  Ahmad, Irfan  and  Lydia, Maya Silvi  and  Dadure, Pankaj},
  title     = {Findings of the WMT 2026 Shared Task on Low-Resource Arabic-Asian Language Translation},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {1124--1137},
  abstract  = {We present the findings of the WMT 2026, which evaluated bidirectional translation between Arabic and English, Hindi, Bangla, Indonesian, and Urdu. We release AraAsian1.0, a news-domain parallel corpus with 20.1K-21.0K training pairs per language pair, and evaluate submissions on ten translation directions using BLEU, ChrF2, TER, COMET, and BERT-based metrics. Of 23 registered teams, 13 submitted systems, producing 70 primary and 65 contrastive runs. Fine-tuned multilingual MT models remained the strongest overall: MADLAD-based systems won four primary directions, while target-aware pivoting, joint multilingual adaptation, and MBR-based ensembling led the remaining tracks. The best contrastive run exceeded the best primary run in only three of ten directions, and each gain was below 0.2 BLEU, indicating that additional data, prompting, or multi-pass refinement did not provide consistent improvements. Bangla had the lowest winning BLEU in both directions, despite comparable training-set size. BLEU and COMET selected the same winner in nine of ten primary results but only six of ten contrastive results, with the largest disagreements occurring for LLM-based and reranked outputs. These results emphasize domain-matched adaptation, target-aware transfer, and multi-metric reporting in low-resource Arabic-Asian translation. We made the AraAsian1.0 dataset available for the community.},
  url       = {https://aclanthology.org/2026.wmt-1.54}
}