Interpretability for Language Learners Using Example-Based Grammatical Error Correction

• URL: http://arxiv.org/abs/2203.07085v1
• Date: Mon, 14 Mar 2022 13:15:00 GMT
• Title: Interpretability for Language Learners Using Example-Based Grammatical Error Correction
• Authors: Masahiro Kaneko, Sho Takase, Ayana Niwa, Naoaki Okazaki
• Abstract summary: We introduce an Example-Based GEC (EB-GEC) that presents examples to language learners as a basis for a correction result. Experiments demonstrate that the examples presented by EB-GEC help language learners decide to accept or refuse suggestions from the GEC output.
• Score: 27.850970793739933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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