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The Korean Association for the Study of English Language and Linguistics
[ Article ]
Korea Journal of English Language and Linguistics - Vol. 24, No. 0, pp.568-588
ISSN: 1598-1398 (Print) 2586-7474 (Online)
Print publication date 31 Jan 2024
Received 01 Feb 2024 Revised 11 Apr 2024 Accepted 19 Jun 2024
DOI: https://doi.org/10.15738/kjell.24..202406.568

Investigating Grammatical Transfer in Korean-English GPT2 Language Models

Keonwoo Koo ; Jaemin Lee ; Myung-Kwan Park
(co-1st author) Ph.D. Candidate, Department of English, Dongguk University qjelrjsdn@naver.com
(co-1st author) MA Candidate, Department of English, Dongguk University whd7987@gmail.com
(corresponding author) Professor, Department of English, Dongguk University parkmk@dgu.edu


© 2024 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.

Abstract

With the recent success of artificial neural language model (LMs), their language acquisition has gained much attention (Futrell et al. 2019, Hu et al. 2020, Linzen et al. 2016, Warstadt et al. 2020, Wilcox et al. 2018). This paper delves into their second language (L2) acquisition, a largely unexplored area compared to their first language (L1) learning. The primary focus is on unraveling transfer effects originating from the L1’s linguistic structures. By closely examining our LMs’ performances on English grammar tasks, this study inspects how LMs encode abstract grammatical knowledge, particularly how pre-training biases acquired from Korean (L1) influence English (L2) performances in LMs. We present exploratory experiments where LMs were first trained on the dataset representing the initial language acquisition stage, followed by fine-tuning on the second language dataset. We analyzed cross-lingual transfer effects across diverse linguistic phenomena with the BLiMP test suite. We found that L1 pre-training did not accelerate linguistic generalization in the second language. Furthermore, our results revealed significant L1-interference, where the initial language knowledge hindered the LMs' ability to acquire and apply second language rules.

Keywords:

second language acquisition, neural language model, GPT-2, transfer effects, L1-interference

Acknowledgments

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

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