The Korean Association for the Study of English Language and Linguistics

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Korean Journal of English Language and Linguistics - Vol. 21

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Korea Journal of English Language and Linguistics - Vol. 21, No. 0, pp.487-509
Abbreviation: KASELL
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Received 25 Apr 2021 Revised 05 Jun 2021 Accepted 25 Jun 2021

A Deep Learning-based Understanding of Nativelikeness: A Linguistic Perspective
Kwonsik Park ; Sanghoun Song
(1st author) Graduate Student, Dept. of Linguistics, Korea Univ., Tel: +82-2-3290-1648 (
(corresponding author) Professor, Dept. of Linguistics, Korea Univ., Tel: +82-2-3290-2177 (

© 2021 KASELL All rights reserved
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.
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Constructing deep learning models that identify nativelikeness in English sentences, this paper addresses two relevant research questions: is nativelikeness measurable, and is it determined by syntactic well-formedness and lexical associations? To address the first, our models are evaluated by judging every item in Test Suite I, which comprises learner and native sentences from four sources. The results show that the models predict nativelikeness reasonably well. Next, syntactic well-formedness is examined via Test Suite II, comprising correct–incorrect minimal pairs with two conditions. The results indicate that our models do not satisfactorily detect it. The learners’ results reveal their limited knowledge, suggesting that the models learn the inadequateness of lexical associations as a feature of non-nativelikeness because the learner training data comprises Korean English learner corpora. However, our models’ results also show poor performance. We conclude that deep learning is capable of measuring nativelikeness, and well-formedness and lexical associations are no more than necessary conditions for nativelikeness. This implies the need to consider other factors when defining and assessing nativelikeness.

Keywords: deep learning, nativelikeness, well-formedness, lexical association, learner corpora


This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2020S1A5A2A03042760).

Some earlier versions of the present study were partially presented at the 34th Pacific Asia Conference on Language, Information and Computation (online, Oct-24-2020) and the 2020 International Conference on English Linguistics (online, Oct-17-2020).

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