@inproceedings{aldabbas-etal-2026-farabi,
title = "{FARABI} at {WMT} 2026: Post-Hoc Channel Controllers for Low-Resource {A}rabic{--}{A}sian Machine Translation",
author = "Aldabbas, Farizeh and
Altahan, Zyad and
Elsafty, Hossam and
Sifa, Rafet",
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.166/",
pages = "2255--2265",
ISBN = "979-8-89176-474-3",
abstract = "We describe our submission to the WMT26 Low-Resource Arabic{--}Asian Language Translation shared task, covering ten translation directions between Arabic and five Asian languages. Our central question is whether a post-hoc controller can improve translation quality beyond a fine-tuned backbone without modifying any model weights. For the five into-Arabic directions, our primary system jointly fine-tunes facebook/nllb-200-3.3B on all source languages and augments it at inference time with per-direction NTK-Mirror controllers, each containing fewer than 6K parameters, that rescale frozen transformer output channels. For the five from-Arabic directions, we use Qwen2.5-7B-Instruct as the backbone, adapted via QLoRA and likewise augmented with NTK-Mirror. In the official evaluation, our system ranked first on Bengali-to-Arabic and Indonesian-to-Arabic, second on Hindi-to-Arabic and Urdu-to-Arabic, and third on English-to-Arabic, placing in the top three across all five into-Arabic directions. Post-submission analysis indicates that the weaker results in the from-Arabic directions stem primarily from a model-selection decision made at submission time."
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<abstract>We describe our submission to the WMT26 Low-Resource Arabic–Asian Language Translation shared task, covering ten translation directions between Arabic and five Asian languages. Our central question is whether a post-hoc controller can improve translation quality beyond a fine-tuned backbone without modifying any model weights. For the five into-Arabic directions, our primary system jointly fine-tunes facebook/nllb-200-3.3B on all source languages and augments it at inference time with per-direction NTK-Mirror controllers, each containing fewer than 6K parameters, that rescale frozen transformer output channels. For the five from-Arabic directions, we use Qwen2.5-7B-Instruct as the backbone, adapted via QLoRA and likewise augmented with NTK-Mirror. In the official evaluation, our system ranked first on Bengali-to-Arabic and Indonesian-to-Arabic, second on Hindi-to-Arabic and Urdu-to-Arabic, and third on English-to-Arabic, placing in the top three across all five into-Arabic directions. Post-submission analysis indicates that the weaker results in the from-Arabic directions stem primarily from a model-selection decision made at submission time.</abstract>
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%0 Conference Proceedings
%T FARABI at WMT 2026: Post-Hoc Channel Controllers for Low-Resource Arabic–Asian Machine Translation
%A Aldabbas, Farizeh
%A Altahan, Zyad
%A Elsafty, Hossam
%A Sifa, Rafet
%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 aldabbas-etal-2026-farabi
%X We describe our submission to the WMT26 Low-Resource Arabic–Asian Language Translation shared task, covering ten translation directions between Arabic and five Asian languages. Our central question is whether a post-hoc controller can improve translation quality beyond a fine-tuned backbone without modifying any model weights. For the five into-Arabic directions, our primary system jointly fine-tunes facebook/nllb-200-3.3B on all source languages and augments it at inference time with per-direction NTK-Mirror controllers, each containing fewer than 6K parameters, that rescale frozen transformer output channels. For the five from-Arabic directions, we use Qwen2.5-7B-Instruct as the backbone, adapted via QLoRA and likewise augmented with NTK-Mirror. In the official evaluation, our system ranked first on Bengali-to-Arabic and Indonesian-to-Arabic, second on Hindi-to-Arabic and Urdu-to-Arabic, and third on English-to-Arabic, placing in the top three across all five into-Arabic directions. Post-submission analysis indicates that the weaker results in the from-Arabic directions stem primarily from a model-selection decision made at submission time.
%U https://aclanthology.org/2026.wmt-main.166/
%P 2255-2265
Markdown (Informal)
[FARABI at WMT 2026: Post-Hoc Channel Controllers for Low-Resource Arabic–Asian Machine Translation](https://aclanthology.org/2026.wmt-main.166/) (Aldabbas et al., WMT 2026)
ACL