  Grammatical Error Correction (GEC) should not focus only on high accuracy of
corrections but also on interpretability for language learning. However,
existing neural-based GEC models mainly aim at improving accuracy, and their
interpretability has not been explored. A promising approach for improving
interpretability is an example-based method, which uses similar retrieved
examples to generate corrections. In addition, examples are beneficial in
language learning, helping learners understand the basis of grammatically
incorrect/correct texts and improve their confidence in writing. Therefore, we
hypothesize that incorporating an example-based method into GEC can improve
interpretability as well as support language learners. In this study, we
introduce an Example-Based GEC (EB-GEC) that presents examples to language
learners as a basis for a correction result. The examples consist of pairs of
correct and incorrect sentences similar to a given input and its predicted
correction. Experiments demonstrate that the examples presented by EB-GEC help
language learners decide to accept or refuse suggestions from the GEC output.
Furthermore, the experiments also show that retrieved examples improve the
accuracy of corrections.
