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        <title>UTSK26 at WAT2026 Patent Claims Translation/Evaluation Tasks</title>
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            <title>Proceedings of the Thirteenth Workshop on Asian Translation (WAT 2026)</title>
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    <abstract>This paper describes the UTSK26 submission to the WAT2026 Patent Claims Translation/Evaluation Task for the Japanese-to-English (JA-EN) and English-to-Japanese (EN-JA) directions. The task requires participants to annotate translation errors in machine translation (MT) outputs of patent claims and to assign a quality score from 0 to 100 to each translation. For error annotation, we use GEMBA-Patent-Hybrid, which combines an LLM-as-a-Judge method for detecting error spans, categories, and severities using GPT-5.2 with another method for assigning categories and severities to encoder-detected error spans. For quality scoring, we use Patent-Rubric-Checklist, a rubric-based LLM-as-a-Judge method that instructs GPT-5.2 to answer patent-specific checklist items covering five evaluation dimensions and aggregates the answers into a score with predefined weights. On the training data, which consist of the human evaluation results of the WAT2025 task, Patent-Rubric-Checklist achieves the highest Pearson and Spearman correlations with human scores in both directions among the evaluated methods, including COMETKiwi, MetricX-24, GEMBA-DA, and GEMBA-MQM.</abstract>
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%0 Conference Proceedings
%T UTSK26 at WAT2026 Patent Claims Translation/Evaluation Tasks
%A Fu, Yingyi
%A Buma, Kosei
%A Utsuro, Takehito
%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 fu-etal-2026-utsk26
%X This paper describes the UTSK26 submission to the WAT2026 Patent Claims Translation/Evaluation Task for the Japanese-to-English (JA-EN) and English-to-Japanese (EN-JA) directions. The task requires participants to annotate translation errors in machine translation (MT) outputs of patent claims and to assign a quality score from 0 to 100 to each translation. For error annotation, we use GEMBA-Patent-Hybrid, which combines an LLM-as-a-Judge method for detecting error spans, categories, and severities using GPT-5.2 with another method for assigning categories and severities to encoder-detected error spans. For quality scoring, we use Patent-Rubric-Checklist, a rubric-based LLM-as-a-Judge method that instructs GPT-5.2 to answer patent-specific checklist items covering five evaluation dimensions and aggregates the answers into a score with predefined weights. On the training data, which consist of the human evaluation results of the WAT2025 task, Patent-Rubric-Checklist achieves the highest Pearson and Spearman correlations with human scores in both directions among the evaluated methods, including COMETKiwi, MetricX-24, GEMBA-DA, and GEMBA-MQM.
%U https://aclanthology.org/2026.wat-1.8/
%P 119-133

Markdown (Informal)

[UTSK26 at WAT2026 Patent Claims Translation/Evaluation Tasks](https://aclanthology.org/2026.wat-1.8/) (Fu et al., WAT 2026)

• UTSK26 at WAT2026 Patent Claims Translation/Evaluation Tasks (Fu et al., WAT 2026)

ACL

• Yingyi Fu, Kosei Buma, and Takehito Utsuro. 2026. UTSK26 at WAT2026 Patent Claims Translation/Evaluation Tasks. In Proceedings of the Thirteenth Workshop on Asian Translation (WAT 2026), pages 119–133, Hengqin, China. Association for Computational Linguistics.