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    <abstract>This paper describes Laniqo’s submission to the WMT26 Video Subtitle Translation Shared Task, translating subtitles from Simplified Chinese into English, Thai, Indonesian, Malay, and Traditional Chinese under a 20B-parameter, open-license model constraint. Our system optimizes translation quality and subtitle compliance entirely at inference time, without fine-tuning, by combining hedged multi-prompt candidate generation, language-identification and compliance pruning, multi-objective Pareto reranking, reasoning-based candidate fusion, and a fallback/compression step. We screened four open-weight models and compared the two strongest, Qwen3.5-9B and Gemma-4-12B-it, across the full pipeline on the complete test corpus. Gemma-4-12B-it reached significantly higher compliance rates in every target language, thus it was chosen as our final system.</abstract>
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%0 Conference Proceedings
%T Laniqo at WMT26 Video Subtitle Translation Shared Task: Multi-Objective Fusion of Pareto-Optimal Candidates
%A Guttmann, Kamil
%A Nowakowski, Artur
%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 guttmann-nowakowski-2026-laniqo
%X This paper describes Laniqo’s submission to the WMT26 Video Subtitle Translation Shared Task, translating subtitles from Simplified Chinese into English, Thai, Indonesian, Malay, and Traditional Chinese under a 20B-parameter, open-license model constraint. Our system optimizes translation quality and subtitle compliance entirely at inference time, without fine-tuning, by combining hedged multi-prompt candidate generation, language-identification and compliance pruning, multi-objective Pareto reranking, reasoning-based candidate fusion, and a fallback/compression step. We screened four open-weight models and compared the two strongest, Qwen3.5-9B and Gemma-4-12B-it, across the full pipeline on the complete test corpus. Gemma-4-12B-it reached significantly higher compliance rates in every target language, thus it was chosen as our final system.
%U https://aclanthology.org/2026.wmt-main.204/
%P 2608-2615

Markdown (Informal)

[Laniqo at WMT26 Video Subtitle Translation Shared Task: Multi-Objective Fusion of Pareto-Optimal Candidates](https://aclanthology.org/2026.wmt-main.204/) (Guttmann & Nowakowski, WMT 2026)

• Laniqo at WMT26 Video Subtitle Translation Shared Task: Multi-Objective Fusion of Pareto-Optimal Candidates (Guttmann & Nowakowski, WMT 2026)

ACL

• Kamil Guttmann and Artur Nowakowski. 2026. Laniqo at WMT26 Video Subtitle Translation Shared Task: Multi-Objective Fusion of Pareto-Optimal Candidates. In Proceedings of the Eleventh Conference on Machine Translation, pages 2608–2615, Budapest, Hungary. Association for Computational Linguistics.