A BERT-Based Automatic Scoring Model of Korean Language Learners' Essay

Jung Hee Lee, Ji Su Park, Jin Gon Shon

Research output: Contribution to journalArticlepeer-review


This research applies a pre-trained bidirectional encoder representations from transformers (BERT) handwriting recognition model to predict foreign Korean-language learners’ writing scores. A corpus of 586 answers to midterm and final exams written by foreign learners at the Intermediate 1 level was acquired and used for pre-training, resulting in consistent performance, even with small datasets. The test data were pre-processed and fine-tuned, and the results were calculated in the form of a score prediction. The difference between the prediction and actual score was then calculated. An accuracy of 95.8% was demonstrated, indicating that the prediction results were strong overall; hence, the tool is suitable for the automatic scoring of Korean written test answers, including grammatical errors, written by foreigners.

Original languageEnglish
Pages (from-to)282-291
Number of pages10
JournalJournal of Information Processing Systems
Issue number2
Publication statusPublished - Apr 2022


  • Automatic writing scoring
  • Bidirectional encoder representations from transformers
  • Korean as a foreign language
  • Natural language processing

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