@InProceedings{singh-EtAl:2026:wmt,
  author    = {Singh, Sumit  and  Vishwakarma, Anish Kumar  and  Sahu, Brijmohan Lal  and  Kumar, Ashwani},
  title     = {UPES\_NLP at WMT 2026 Low-Resource Arabic--Asian Machine Translation Shared Task: Arabic-Centric Machine Translation with NLLB-200-MOE and Prompt-Constrained Entity-Preserving Translation with GPT-5-mini},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {2345--2350},
  abstract  = {This paper presents the UPES\_NLP submission to the WMT 2026 Low-Resource Arabic–Asian Machine Translation Shared Task. We used the NLLB-200 Mixture-of-Experts model for translation inference and GPT-5-mini with entity-aware prompting to preserve named entities. Our system participated in both Primary and Contrastive tracks across multiple language pairs. It ranked 3rd in Arabic→English with a BLEU score of 32.08 and 2nd in Arabic→Indonesian with a BLEU score of 22.77. The results show that the proposed approach performs competitively across low-resource Arabic–Asian translation tasks.},
  url       = {https://aclanthology.org/2026.wmt-1.176}
}