SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction

Abstract

We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction. Previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluation of the system output because there is no dataset of system output with manual evaluation. This study manually evaluates the output of grammatical error correction systems to optimize the metrics. Experimental results show that the proposed metric improves the correlation with manual evaluation in both system- and sentence-level meta-evaluation. Our dataset and metric will be made publicly available.