@InProceedings{huang-EtAl:2026:wmt,
  author    = {Huang, Boqi  and  Wei, Daimeng  and  GUO, Jiaxin  and  Luo, Yuanchang  and  Shang, Hengchao  and  Li, Zongyao  and  Yang, Jinlong  and  Wu, Zhanglin  and  He, Yu  and  Lan, Xiaoqing},
  title     = {HW-TSC's Submission to the WMT26 Terminology Translation Shared Task},
  booktitle      = {Proceedings of the Eleventh Conference on Machine Translation},
  month          = {October},
  year           = {2026},
  address        = {Budapest, Hungary},
  publisher      = {Association for Computational Linguistics},
  pages     = {1808--1813},
  abstract  = {We describe HW-TSC's submissions to both tracks and all language pairs of the WMT26 Terminology Translation Shared Task. Our primary system uses Qwen3.7-Max for source-term extraction, bilingual term alignment, and document translation. In Track 1, it matches the current document or translation chunk against the supplied dictionary and places at most 200 applicable term pairs in the translation prompt. In Track 2, it first induces a bilingual glossary from the seed bitexts and then reuses the Track 1 translation module. Induction combines open source-term extraction with seed-source expression matching, followed by LLM alignment to the seed targets. We optimize the extraction prompt with SkillOpt on a train/validation/test split of the WMT25 English–Chinese finance data and use Qwen3.7-Max to rewrite it for directions without labeled optimization data. Extraction recall rises from 0.6250 to 0.6344 on validation and from 0.6451 to 0.6674 on the held-out test split. On the same held-out test documents with Qwen3.7-Max, glossary induction raises TSR from 0.5212 (no glossary) to 0.7910 with the initial prompt and to 0.8026 after SkillOpt, with chrF++ of 57.45, 67.05, and 67.36; the official glossary yields TSR 0.9019 and chrF++ 63.94.},
  url       = {https://aclanthology.org/2026.wmt-1.115}
}