@inproceedings{yadav-kuila-2026-code,
title = "Code {\&} Corpus at {WMT} 2026: Checkpoint-Pool Minimum {B}ayes Risk Decoding for {A}rabic-{H}indi and {A}rabic-{B}angla Translation",
author = "Yadav, Raman and
Kuila, Alapan",
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.177/",
pages = "2344--2350",
ISBN = "979-8-89176-474-3",
abstract = "We present a constrained submission to the WMT 2026 Low-Resource Arabic-Asian Language Translation shared task for Arabic{--}Hindi and Arabic{--}Bangla translation. Our system fine-tunes the NLLB-200 distilled 600M model jointly on all four translation directions using the official task data. At inference, instead of relying on a single best checkpoint, we retain multiple training checkpoints and generate candidate translations from each checkpoint. We then apply checkpoint-pool Minimum Bayes Risk (MBR) decoding with chrF as the utility function to select the final translation. This approach exploits diversity across training checkpoints without additional training runs. On the official evaluation, our primary system ranked 4th of 8 for Hindi{\textrightarrow}Arabic, 3rd of 6 for Bangla{\textrightarrow}Arabic, 3rd of 10 for Arabic{\textrightarrow}Hindi, and 2nd of 7 for Arabic{\textrightarrow}Bangla."
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<abstract>We present a constrained submission to the WMT 2026 Low-Resource Arabic-Asian Language Translation shared task for Arabic–Hindi and Arabic–Bangla translation. Our system fine-tunes the NLLB-200 distilled 600M model jointly on all four translation directions using the official task data. At inference, instead of relying on a single best checkpoint, we retain multiple training checkpoints and generate candidate translations from each checkpoint. We then apply checkpoint-pool Minimum Bayes Risk (MBR) decoding with chrF as the utility function to select the final translation. This approach exploits diversity across training checkpoints without additional training runs. On the official evaluation, our primary system ranked 4th of 8 for Hindi→Arabic, 3rd of 6 for Bangla→Arabic, 3rd of 10 for Arabic→Hindi, and 2nd of 7 for Arabic→Bangla.</abstract>
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%0 Conference Proceedings
%T Code & Corpus at WMT 2026: Checkpoint-Pool Minimum Bayes Risk Decoding for Arabic-Hindi and Arabic-Bangla Translation
%A Yadav, Raman
%A Kuila, Alapan
%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 yadav-kuila-2026-code
%X We present a constrained submission to the WMT 2026 Low-Resource Arabic-Asian Language Translation shared task for Arabic–Hindi and Arabic–Bangla translation. Our system fine-tunes the NLLB-200 distilled 600M model jointly on all four translation directions using the official task data. At inference, instead of relying on a single best checkpoint, we retain multiple training checkpoints and generate candidate translations from each checkpoint. We then apply checkpoint-pool Minimum Bayes Risk (MBR) decoding with chrF as the utility function to select the final translation. This approach exploits diversity across training checkpoints without additional training runs. On the official evaluation, our primary system ranked 4th of 8 for Hindi→Arabic, 3rd of 6 for Bangla→Arabic, 3rd of 10 for Arabic→Hindi, and 2nd of 7 for Arabic→Bangla.
%U https://aclanthology.org/2026.wmt-main.177/
%P 2344-2350
Markdown (Informal)
[Code & Corpus at WMT 2026: Checkpoint-Pool Minimum Bayes Risk Decoding for Arabic-Hindi and Arabic-Bangla Translation](https://aclanthology.org/2026.wmt-main.177/) (Yadav & Kuila, WMT 2026)
ACL