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    <abstract>This paper presents the results and findings of the 2nd Patent Claims Translation/Evaluation Task at WAT2026. Building on the first task, which collected human error annotations for machine-translated patent claims, this year we introduce an evaluation task: given the source claims and the machine translations gathered last year, participants automatically annotate translation errors (spans, categories, and severities) and assign a holistic quality score to each translation. We received submissions from three teams covering the Japanese–English (JA–EN) and English–Japanese (EN–JA) directions. We evaluated the submissions against the organizers’ reference annotations in terms of error-span detection, severity, and quality-score correlation and correlated the results with the design of each participating system. Our analysis highlights two observations. First, giving an LLM explicit word alignments mainly helps it detect errors that hinge on the source–target correspondence (mistranslation and terminology), while leaving target-only fluency errors unaffected. Second, locating errors and producing a good overall quality score behave as largely independent abilities: the system whose score correlated best with the reference detected the fewest errors, because it scored quality with a module separate from its error annotator.</abstract>
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%0 Conference Proceedings
%T Findings of the 2nd Patent Claims Translation/Evaluation Task at WAT 2026
%A Nakazawa, Toshiaki
%A Tsunakawa, Takashi
%A Goto, Isao
%A Sudoh, Katsuhito
%A Okuyama, Shoichi
%A Ieda, Takashi
%A Nagata, Masaaki
%Y Nakazawa, Toshiaki
%Y Goto, Isao
%S Proceedings of the Thirteenth Workshop on Asian Translation (WAT 2026)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-446-0
%F nakazawa-etal-2026-findings
%X This paper presents the results and findings of the 2nd Patent Claims Translation/Evaluation Task at WAT2026. Building on the first task, which collected human error annotations for machine-translated patent claims, this year we introduce an evaluation task: given the source claims and the machine translations gathered last year, participants automatically annotate translation errors (spans, categories, and severities) and assign a holistic quality score to each translation. We received submissions from three teams covering the Japanese–English (JA–EN) and English–Japanese (EN–JA) directions. We evaluated the submissions against the organizers’ reference annotations in terms of error-span detection, severity, and quality-score correlation and correlated the results with the design of each participating system. Our analysis highlights two observations. First, giving an LLM explicit word alignments mainly helps it detect errors that hinge on the source–target correspondence (mistranslation and terminology), while leaving target-only fluency errors unaffected. Second, locating errors and producing a good overall quality score behave as largely independent abilities: the system whose score correlated best with the reference detected the fewest errors, because it scored quality with a module separate from its error annotator.
%U https://aclanthology.org/2026.wat-1.11/
%P 155-163

Markdown (Informal)

[Findings of the 2nd Patent Claims Translation/Evaluation Task at WAT 2026](https://aclanthology.org/2026.wat-1.11/) (Nakazawa et al., WAT 2026)

• Findings of the 2nd Patent Claims Translation/Evaluation Task at WAT 2026 (Nakazawa et al., WAT 2026)

ACL

• Toshiaki Nakazawa, Takashi Tsunakawa, Isao Goto, Katsuhito Sudoh, Shoichi Okuyama, Takashi Ieda, and Masaaki Nagata. 2026. Findings of the 2nd Patent Claims Translation/Evaluation Task at WAT 2026. In Proceedings of the Thirteenth Workshop on Asian Translation (WAT 2026), pages 155–163, Hengqin, China. Association for Computational Linguistics.