@inproceedings{karim-etal-2026-nerds,
title = "{NERDS}-{NITS} at {WMT}2026: Pivot-Based and Direct Neural Machine Translation for {A}rabic{--}{A}sian Low-Resource Language Pairs",
author = "Karim, Misbahul and
Ahmed, Faruk and
Islam, Mishbahul and
Laskar, Sahinur and
Laskar, Rabul",
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.173/",
pages = "2318--2323",
ISBN = "979-8-89176-474-3",
abstract = "This paper presents NERDS-NITS{'}s sub mission to the WMT 2026 Low-Resource Arabic{--}Asian Language Translation Shared Task, which covers the following language pairs: Arabic{\ensuremath{\leftrightarrow}}English, Arabic{\ensuremath{\leftrightarrow}}Hindi, Arabic{\ensuremath{\leftrightarrow}}Bengali, Arabic{\ensuremath{\leftrightarrow}}Urdu, and Arabic{\ensuremath{\leftrightarrow}}Indonesian. For the three Indic language pairs, we propose a pivot-based framework that combines a fine-tuned Arabic{\ensuremath{\leftrightarrow}}English NLLB-200 model with pretrained IndicTrans2 checkpoints, motivated by the scarcity of direct Arabic{--}Indic parallel data. For Arabic{\ensuremath{\leftrightarrow}}Indonesian, where Indic Trans2 offers no coverage, we instead fine-tune NLLB-200-1.3B directly using QLoRA under memory-constrained conditions. Our Arabic{\ensuremath{\leftrightarrow}}English backbone, built on fine-tuned NLLB-200-600M model, forms the pivot leg for all Indic-language systems. Across all evaluated pairs, fine-tuning yields consistent improvements over zero-shot baselines, and the proposed pivot architecture further outperforms direct multilingual fine-tuning for Arabic{--}Indic pairs, with gains of up to 6.68 BLEU and 0.02 COMET"
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<abstract>This paper presents NERDS-NITS’s sub mission to the WMT 2026 Low-Resource Arabic–Asian Language Translation Shared Task, which covers the following language pairs: Arabic\ensuremathłeftrightarrowEnglish, Arabic\ensuremathłeftrightarrowHindi, Arabic\ensuremathłeftrightarrowBengali, Arabic\ensuremathłeftrightarrowUrdu, and Arabic\ensuremathłeftrightarrowIndonesian. For the three Indic language pairs, we propose a pivot-based framework that combines a fine-tuned Arabic\ensuremathłeftrightarrowEnglish NLLB-200 model with pretrained IndicTrans2 checkpoints, motivated by the scarcity of direct Arabic–Indic parallel data. For Arabic\ensuremathłeftrightarrowIndonesian, where Indic Trans2 offers no coverage, we instead fine-tune NLLB-200-1.3B directly using QLoRA under memory-constrained conditions. Our Arabic\ensuremathłeftrightarrowEnglish backbone, built on fine-tuned NLLB-200-600M model, forms the pivot leg for all Indic-language systems. Across all evaluated pairs, fine-tuning yields consistent improvements over zero-shot baselines, and the proposed pivot architecture further outperforms direct multilingual fine-tuning for Arabic–Indic pairs, with gains of up to 6.68 BLEU and 0.02 COMET</abstract>
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%0 Conference Proceedings
%T NERDS-NITS at WMT2026: Pivot-Based and Direct Neural Machine Translation for Arabic–Asian Low-Resource Language Pairs
%A Karim, Misbahul
%A Ahmed, Faruk
%A Islam, Mishbahul
%A Laskar, Sahinur
%A Laskar, Rabul
%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 karim-etal-2026-nerds
%X This paper presents NERDS-NITS’s sub mission to the WMT 2026 Low-Resource Arabic–Asian Language Translation Shared Task, which covers the following language pairs: Arabic\ensuremathłeftrightarrowEnglish, Arabic\ensuremathłeftrightarrowHindi, Arabic\ensuremathłeftrightarrowBengali, Arabic\ensuremathłeftrightarrowUrdu, and Arabic\ensuremathłeftrightarrowIndonesian. For the three Indic language pairs, we propose a pivot-based framework that combines a fine-tuned Arabic\ensuremathłeftrightarrowEnglish NLLB-200 model with pretrained IndicTrans2 checkpoints, motivated by the scarcity of direct Arabic–Indic parallel data. For Arabic\ensuremathłeftrightarrowIndonesian, where Indic Trans2 offers no coverage, we instead fine-tune NLLB-200-1.3B directly using QLoRA under memory-constrained conditions. Our Arabic\ensuremathłeftrightarrowEnglish backbone, built on fine-tuned NLLB-200-600M model, forms the pivot leg for all Indic-language systems. Across all evaluated pairs, fine-tuning yields consistent improvements over zero-shot baselines, and the proposed pivot architecture further outperforms direct multilingual fine-tuning for Arabic–Indic pairs, with gains of up to 6.68 BLEU and 0.02 COMET
%U https://aclanthology.org/2026.wmt-main.173/
%P 2318-2323
Markdown (Informal)
[NERDS-NITS at WMT2026: Pivot-Based and Direct Neural Machine Translation for Arabic–Asian Low-Resource Language Pairs](https://aclanthology.org/2026.wmt-main.173/) (Karim et al., WMT 2026)
ACL