Interpretability for Language Learners Using Example-Based Grammatical Error Correction

Abstract

Grammatical Error Correction (GEC) should focus not only on correction accuracy but also on the interpretability of the results for language learners. However, existing neural-based GEC models mostly focus on improving accuracy, while their interpretability has not been explored. Example-based methods are promising for improving interpretability, which use similar retrieved examples to generate corrections. Furthermore, examples are beneficial in language learning, helping learners to understand the basis for grammatically incorrect/correct texts and improve their confidence in writing. Therefore, we hypothesized that incorporating an example-based method into GEC could improve interpretability and support language learners. In this study, we introduce an Example-Based GEC (EB-GEC) that presents examples to language learners as a basis for 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 whether to accept or refuse suggestions from the GEC output. Furthermore, the experiments show that retrieved examples also improve the accuracy of corrections.