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BERTOEIC: Solving TOEIC Problems Using Simple and Efficient Data Augmentation Techniques with Pretrained Transformer Encodersopen access

Authors
Lee, JeongwooMoon, HyeonseokPark, ChanjunSeo, JaehyungEo, SugyeongLim, Heuiseok
Issue Date
7월-2022
Publisher
MDPI
Keywords
artificial intelligence; deep learning; natural language processing; machine reading comprehension; data augmentation
Citation
APPLIED SCIENCES-BASEL, v.12, no.13
Indexed
SCIE
SCOPUS
Journal Title
APPLIED SCIENCES-BASEL
Volume
12
Number
13
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/142933
DOI
10.3390/app12136686
ISSN
2076-3417
Abstract
Recent studies have attempted to understand natural language and infer answers. Machine reading comprehension is one of the representatives, and several related datasets have been opened. However, there are few official open datasets for the Test of English for International Communication (TOEIC), which is widely used for evaluating people's English proficiency, and research for further advancement is not being actively conducted. We consider that the reason why deep learning research for TOEIC is difficult is due to the data scarcity problem, so we therefore propose two data augmentation methods to improve the model in a low resource environment. Considering the attributes of the semantic and grammar problem type in TOEIC, the proposed methods can augment the data similar to the real TOEIC problem by using POS-tagging and Lemmatizing. In addition, we confirmed the importance of understanding semantics and grammar in TOEIC through experiments on each proposed methodology and experiments according to the amount of data. The proposed methods address the data shortage problem of TOEIC and enable an acceptable human-level performance.
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