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

Jeongwoo Lee, Hyeonseok Moon, Chanjun Park, Jaehyung Seo, Sugyeong Eo, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number6686
JournalApplied Sciences (Switzerland)
Issue number13
Publication statusPublished - 2022 Jul 1

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.


  • artificial intelligence
  • data augmentation
  • deep learning
  • machine reading comprehension
  • natural language processing

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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