@inproceedings{singh-etal-2026-upes,
title = "{UPES}{\_}{NLP} at {WMT} 2026 Low-Resource {A}rabic{--}{A}sian Machine Translation Shared Task: {A}rabic-Centric Machine Translation with {NLLB}-200-{MOE} and Prompt-Constrained Entity-Preserving Translation with {GPT}-5-mini",
author = "Singh, Sumit and
Vishwakarma, Anish and
Sahu, Brijmohan and
Kumar, Ashwani",
editor = "Haddow, Barry and
Kocmi, Tom and
Koehn, Philipp and
Monz, Christof",
booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wmt-main.176/",
pages = "2338--2343",
ISBN = "979-8-89176-474-3",
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{\textrightarrow}English with a BLEU score of 32.08 and 2nd in Arabic{\textrightarrow}Indonesian with a BLEU score of 22.77. The results show that the proposed approach performs competitively across low-resource Arabic{--}Asian translation tasks."
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<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.</abstract>
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%0 Conference Proceedings
%T 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
%A Singh, Sumit
%A Vishwakarma, Anish
%A Sahu, Brijmohan
%A Kumar, Ashwani
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F singh-etal-2026-upes
%X 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.
%U https://aclanthology.org/2026.wmt-main.176/
%P 2338-2343
Markdown (Informal)
[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](https://aclanthology.org/2026.wmt-main.176/) (Singh et al., WMT 2026)
ACL