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            <title>Proceedings of the Eleventh Conference on Machine Translation</title>
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            <namePart type="given">Christof</namePart>
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    <abstract>The paper introduces QwenSub-MT, a system that is context-aware and constraint-controlled and has been developed for the WMT 2026 Video Subtitle Translation shared task. It carried out the translation of Simplified Chinese subtitles into 6 different languages. QwenSub-MT used Qwen3.5-Instruct together with a multistage 4-bit QLoRA adaptation and treated adjacent subtitle cues as contextual blocks. In order to enhance consistency and disambiguation, the system incorporated video synopsis information, previous translations, terminology memory and selective visual grounding. A revision stage was carried out to correct semantic, terminology and formatting errors, while duration-aware length control, line-break optimization and deterministic SRT validation were used to make sure that the outputs were readable and structurally valid. 500 subtitle files were submitted in five language directions and obtained a macro-average score of 58.459, placing sixth in the overall ranking. The results show that contextual modelling and structural control are useful, but candidate diversity, independent quality estimation and stronger adaptation to the target language still needs improvement.</abstract>
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%0 Conference Proceedings
%T QwenSub-MT Framework for WMT 2026 Video Subtitle Translation
%A Arora, Palak
%A Jangid, Mansi
%A Nathani, Bharti
%A Joshi, Nisheeth
%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 arora-etal-2026-qwensub
%X The paper introduces QwenSub-MT, a system that is context-aware and constraint-controlled and has been developed for the WMT 2026 Video Subtitle Translation shared task. It carried out the translation of Simplified Chinese subtitles into 6 different languages. QwenSub-MT used Qwen3.5-Instruct together with a multistage 4-bit QLoRA adaptation and treated adjacent subtitle cues as contextual blocks. In order to enhance consistency and disambiguation, the system incorporated video synopsis information, previous translations, terminology memory and selective visual grounding. A revision stage was carried out to correct semantic, terminology and formatting errors, while duration-aware length control, line-break optimization and deterministic SRT validation were used to make sure that the outputs were readable and structurally valid. 500 subtitle files were submitted in five language directions and obtained a macro-average score of 58.459, placing sixth in the overall ranking. The results show that contextual modelling and structural control are useful, but candidate diversity, independent quality estimation and stronger adaptation to the target language still needs improvement.
%U https://aclanthology.org/2026.wmt-main.202/
%P 2584-2591

Markdown (Informal)

[QwenSub-MT Framework for WMT 2026 Video Subtitle Translation](https://aclanthology.org/2026.wmt-main.202/) (Arora et al., WMT 2026)

• QwenSub-MT Framework for WMT 2026 Video Subtitle Translation (Arora et al., WMT 2026)

ACL

• Palak Arora, Mansi Jangid, Bharti Nathani, and Nisheeth Joshi. 2026. QwenSub-MT Framework for WMT 2026 Video Subtitle Translation. In Proceedings of the Eleventh Conference on Machine Translation, pages 2584–2591, Budapest, Hungary. Association for Computational Linguistics.