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Don't Count the Edits, Judge by the Outcome Alone: Reward-Based Evaluation for Grammatical Error Correction

Published 16 Sept 2026arXiv:2609.15559

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Updated 12 h ago · first seen 15 Sept 2026

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Abstract

Grammatical error correction (GEC) evaluation has traditionally relied on reference or edit overlap, which can penalize valid rewrites that differ from gold corrections. Reference-free metrics reduce this dependence, but evaluating whether a fluent output is a valid correction of the source remains challenging. We propose SURE, a source-conditioned reward evaluator trained on within-source preferences spanning minimal-edit and rewrite-oriented corrections. SURE jointly learns an overall reward with criteria-level supervision for grammaticality, faithfulness, and fluency, together with span-level grounding for source-side error resolution. Experiments on SEEDA show that SURE performs competitively against strong baselines, with particular gains on rewrite-style corrections and more disentangled criteria-level diagnostics. Our code is available at https://github.com/hayeonggg/SURE.

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Hayeong RyuSeunguk YuSunhee JoYoungBin Kim

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arXiv (Atom API + RSS)rss.arxiv.org/rss/cs.AI feedT1· Official7 h ago5
arXiv (Atom API + RSS)rss.arxiv.org/rss/cs.CL feedT1· Official7 h ago4

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