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        <title>From Error Detection to Credit Assignment: Do xCOMET Error Spans Provide Useful Policy Credit for Chinese–Malay Translation?</title>
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    <abstract>Sequence-level rewards assign the same group-relative advantage to every generated token, even when only a short translation span is erroneous. We ask whether xCOMET error spans provide useful token-level policy credit for Group Relative Policy Optimization (GRPO) in Chinese–Malay translation. Signed Lexical-Residual GRPO (SLR) adds a bounded, zero-mean local residual to the sequence advantage. Across three matched seeds, SLR improves reward-aligned COMET and MetricX-24, a learned metric not used in training, while chrF++ and BLEU differences remain unresolved. A blinded 300-item evaluation by the bilingual author does not resolve an adequacy or fluency preference and finds a higher omission rate under SLR (9.33% to 16.67%). A repeated span audit finds high sentence-level error detection but weak target-word localization; fewer than half of consistently marked error words receive negative residual credit. In this setting, error detection, localization, and policy-credit actionability are distinct: explainable metric spans change optimization behavior without automatically providing human-aligned token supervision.</abstract>
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%0 Conference Proceedings
%T From Error Detection to Credit Assignment: Do xCOMET Error Spans Provide Useful Policy Credit for Chinese–Malay Translation?
%A Koo, Zi Chen
%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 koo-2026-error
%X Sequence-level rewards assign the same group-relative advantage to every generated token, even when only a short translation span is erroneous. We ask whether xCOMET error spans provide useful token-level policy credit for Group Relative Policy Optimization (GRPO) in Chinese–Malay translation. Signed Lexical-Residual GRPO (SLR) adds a bounded, zero-mean local residual to the sequence advantage. Across three matched seeds, SLR improves reward-aligned COMET and MetricX-24, a learned metric not used in training, while chrF++ and BLEU differences remain unresolved. A blinded 300-item evaluation by the bilingual author does not resolve an adequacy or fluency preference and finds a higher omission rate under SLR (9.33% to 16.67%). A repeated span audit finds high sentence-level error detection but weak target-word localization; fewer than half of consistently marked error words receive negative residual credit. In this setting, error detection, localization, and policy-credit actionability are distinct: explainable metric spans change optimization behavior without automatically providing human-aligned token supervision.
%U https://aclanthology.org/2026.wmt-main.21/
%P 368-379

Markdown (Informal)

[From Error Detection to Credit Assignment: Do xCOMET Error Spans Provide Useful Policy Credit for Chinese–Malay Translation?](https://aclanthology.org/2026.wmt-main.21/) (Koo, WMT 2026)

• From Error Detection to Credit Assignment: Do xCOMET Error Spans Provide Useful Policy Credit for Chinese–Malay Translation? (Koo, WMT 2026)

ACL

• Zi Chen Koo. 2026. From Error Detection to Credit Assignment: Do xCOMET Error Spans Provide Useful Policy Credit for Chinese–Malay Translation?. In Proceedings of the Eleventh Conference on Machine Translation, pages 368–379, Budapest, Hungary. Association for Computational Linguistics.