  Research on Korean grammatical error correction (GEC) is limited, compared to
other major languages such as English. We attribute this problematic
circumstance to the lack of a carefully designed evaluation benchmark for
Korean GEC. In this work, we collect three datasets from different sources
(Kor-Lang8, Kor-Native, and Kor-Learner) that covers a wide range of Korean
grammatical errors. Considering the nature of Korean grammar, We then define 14
error types for Korean and provide KAGAS (Korean Automatic Grammatical error
Annotation System), which can automatically annotate error types from parallel
corpora. We use KAGAS on our datasets to make an evaluation benchmark for
Korean, and present baseline models trained from our datasets. We show that the
model trained with our datasets significantly outperforms the currently used
statistical Korean GEC system (Hanspell) on a wider range of error types,
demonstrating the diversity and usefulness of the datasets. The implementations
and datasets are open-sourced.
