The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 22, No. 0, pp.19-39
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Jan 2022
Received 22 Dec 2021 Revised 24 Jan 2022 Accepted 30 Jan 2022

Assessing Nativelikeness of Korean College Students’ English Writing Using fastText

Hyesun Cho
Associate Professor, Dept. of Education, Graduate School of Education, Dankook Univ.

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This is an open-access article distributed under the terms of the Creative Commons License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.


Neural-network models have recently been used to assess nativelikeness of English sentences written by native or nonnative speakers. In this study, nativelikeness of Korean EFL college students’ English writing is assessed using fastText, a neural-network text classifier using subword information. The training data consisted of English sentences from the corpora of native speakers of English and Korean EFL college students. The test sentences consisted of English writing assignments written by Korean EFL college students. fastText performed well for the task of binary classification into native and nonnative sentences, with high accuracy in less than a minute. The sentences that are classified as native with a high probability tend to have fewer grammatical as well as plausibility errors than those classified as nonnative. For the test sentences, correcting grammatical errors (involving articles, number, subject-verb agreement, voice) had weaker effects on the classification of the sentences than correcting plausibility errors (word choices), which conforms to the previous literature. This suggests that fastText is more sensitive to plausibility errors than grammaticality errors which requires knowledge on hierarchical syntactic structures.


English writing, nativelikeness, Korean EFL learners, fastText, deep learning, neural networks, plausibility, grammaticality


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