Minimally-Augmented Grammatical Error Correction
Roman Grundkiewicz, Marcin Junczys-Dowmunt · 2019
There has been an increased interest in lowresource approaches to automatic grammatical error correction.We introduce Minimally-Augmented Grammatical Error Correction (MAGEC) that does not require any errorlabelled data.Our unsupervised approach is based on a simple but effective synthetic error generation method based on confusion sets from inverted spell-checkers.In low-resource settings, we outperform the current state-ofthe-art results for German and Russian GEC tasks by a large margin without using any real error-annotated training data.When combined with labelled data, our method can serve as an efficient pre-training technique.