Improving Grammatical Error Correction by Correction Acceptability Discrimination

Abstract

Existing Grammatical Error Correction (GEC) methods often overlook the assessment of sentence-level syntax and semantics in the corrected sentence. This oversight results in final corrections that may not be acceptable in the context of the original sentence. In this paper, to improve the performance of Grammatical Error Correction methods, we propose the post-processing task of Correction Acceptability Discrimination (CAD) which aims to remove invalid corrections by comparing the source sentence and its corrected version from the perspective of “sentence-level correctness”. To solve the CAD task, we propose a pipeline method where the acceptability of each possible correction combination based on the predicted corrections for a source sentence will be judged by a discriminator. Within the discriminator, we design a symmetrical comparison operator to overcome the conflicting results that might be caused by the sentence concatenation order. Experiments show that our method can averagely improve F0.5 score by 1.01% over 13 GEC systems in the BEA-2019 test set.

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