Beyond Reference-Based Evaluation: Reward Models for Meta-Evaluation of Grammatical Error Correction
RM-EVAL is introduced, 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 and can be used as a learning signal to improve GEC generation via Reward-Guided Text Generation (RGTG), whic...
Ruo-Tian Wu, Bill Johnson, G. Saunders et al.
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