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        <title>NCI-MT at WMT 2026: A Quality-Gated Cascade for Low-Resource Arabic-Asian Machine Translation</title>
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    <abstract>This paper describes NCI-MT’s submission to the low-resource Arabic-to-Asian language translation task as part of the WMT shared task. Our submission covered six of the ten language pairs. We built two separate systems, the primary and the contrastive. The primary was built using the data provided by WMT. In this approach, we fine-tuned our baseline model (NLLB) with per-direction LoRA adapters. For the contrastive system, we introduced an additional quality-gated refinement stage, which includes two stages: in the first stage, we use a reference-free quality estimator to evaluate the quality of the translations produced by our base model. In the second stage, we iteratively re-translate those translations that are below a certain threshold using LLM-based in-context learning. Our results show that both systems are successful in improving the translation quality in low-resource scenarios.</abstract>
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%0 Conference Proceedings
%T NCI-MT at WMT 2026: A Quality-Gated Cascade for Low-Resource Arabic-Asian Machine Translation
%A Deb, Satarupa
%A Nayak, Prashanth
%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 deb-nayak-2026-nci
%X This paper describes NCI-MT’s submission to the low-resource Arabic-to-Asian language translation task as part of the WMT shared task. Our submission covered six of the ten language pairs. We built two separate systems, the primary and the contrastive. The primary was built using the data provided by WMT. In this approach, we fine-tuned our baseline model (NLLB) with per-direction LoRA adapters. For the contrastive system, we introduced an additional quality-gated refinement stage, which includes two stages: in the first stage, we use a reference-free quality estimator to evaluate the quality of the translations produced by our base model. In the second stage, we iteratively re-translate those translations that are below a certain threshold using LLM-based in-context learning. Our results show that both systems are successful in improving the translation quality in low-resource scenarios.
%U https://aclanthology.org/2026.wmt-main.170/
%P 2298-2303

Markdown (Informal)

[NCI-MT at WMT 2026: A Quality-Gated Cascade for Low-Resource Arabic-Asian Machine Translation](https://aclanthology.org/2026.wmt-main.170/) (Deb & Nayak, WMT 2026)

• NCI-MT at WMT 2026: A Quality-Gated Cascade for Low-Resource Arabic-Asian Machine Translation (Deb & Nayak, WMT 2026)

ACL

• Satarupa Deb and Prashanth Nayak. 2026. NCI-MT at WMT 2026: A Quality-Gated Cascade for Low-Resource Arabic-Asian Machine Translation. In Proceedings of the Eleventh Conference on Machine Translation, pages 2298–2303, Budapest, Hungary. Association for Computational Linguistics.