Beyond Reference-Based Evaluation: Reward Models for Meta-Evaluation of Grammatical Error Correction
Reference-based metrics for Grammatical Error Correction (GEC) such as M$^2$ and ERRANT assume that the reference set enumerates all valid edits, and therefore often penalize corrections that are grammatical and meaning-preserving but phrased differently. We introduce RM-EVAL, a reward model trained on human preference data from SEEDA, as a reference-free meta-evaluator that predicts human-like quality judgments at both full-sequence and partial-sequence levels. Beyond evaluation, we show that the same reward model can be used as a learning signal to improve GEC generation via Reward-Guided Text Generation (RGTG), which keeps a base GEC model frozen and performs online, reward-driven deco...
| Authors | Ruotian Wu, Bill E. Johnson, Gene Saunders et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 September 2026 |
| Last updated at source | 27 September 2026 |
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