Interpretability for Language Learners Using Example-Based Grammatical Error Correction
This addresses the need for interpretability in GEC tools for language learners, though it is incremental as it builds on existing example-based methods.
The paper tackled the problem of making Grammatical Error Correction (GEC) more interpretable for language learners by introducing an example-based method that presents similar correct/incorrect sentence pairs, and found that this approach helps learners decide on corrections and improves correction accuracy.
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.