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    <abstract>We present the findings of the WMT26 Chinese–Southeast Asian Multilingual Machine Translation shared task. The task evaluates fourteen translation directions between Chinese and Thai, Vietnamese, Lao, Burmese, Khmer, Indonesian, and Malay. Organizers released manually aligned parallel data, domain-matched monolingual data, validation data, and a blind test set. The published leaderboard contains eleven systems and combines translation quality, measured by the equal-weight average of sacreBLEU and COMET, with a throughput bonus measured on a shared four-GPU platform. Jiutian-MT ranks first overall, while AiBabelMT achieves the highest throughput. We describe the task design, data resources, evaluation protocol, and published aggregate results, then examine how quality and throughput shape the final ranking. The results also motivate more detailed reporting by language direction and domain</abstract>
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%0 Conference Proceedings
%T Findings of the WMT26 Chinese–Southeast Asian Multilingual MT Shared Task
%A Dong, Tianyu
%A Chen, Ziyan
%A Liu, Jingsong
%A Zhu, Shaolin
%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 dong-etal-2026-findings
%X We present the findings of the WMT26 Chinese–Southeast Asian Multilingual Machine Translation shared task. The task evaluates fourteen translation directions between Chinese and Thai, Vietnamese, Lao, Burmese, Khmer, Indonesian, and Malay. Organizers released manually aligned parallel data, domain-matched monolingual data, validation data, and a blind test set. The published leaderboard contains eleven systems and combines translation quality, measured by the equal-weight average of sacreBLEU and COMET, with a throughput bonus measured on a shared four-GPU platform. Jiutian-MT ranks first overall, while AiBabelMT achieves the highest throughput. We describe the task design, data resources, evaluation protocol, and published aggregate results, then examine how quality and throughput shape the final ranking. The results also motivate more detailed reporting by language direction and domain
%U https://aclanthology.org/2026.wmt-main.55/
%P 1131-1137

Markdown (Informal)

[Findings of the WMT26 Chinese–Southeast Asian Multilingual MT Shared Task](https://aclanthology.org/2026.wmt-main.55/) (Dong et al., WMT 2026)

• Findings of the WMT26 Chinese–Southeast Asian Multilingual MT Shared Task (Dong et al., WMT 2026)

ACL

• Tianyu Dong, Ziyan Chen, Jingsong Liu, and Shaolin Zhu. 2026. Findings of the WMT26 Chinese–Southeast Asian Multilingual MT Shared Task. In Proceedings of the Eleventh Conference on Machine Translation, pages 1131–1137, Budapest, Hungary. Association for Computational Linguistics.