Jung Hee Lee, Ji Su Park, Jin Gon Shon, Journal of Information Processing Systems Vol. 18, No. 2, pp. 282-291, Apr. 2022  

10.3745/JIPS.04.0239
Keywords: Automatic Writing Scoring, Bidirectional Encoder Representations from Transformers, Korean as a Foreign Language, Natural Language Processing
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Abstract

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. These results are particularly meaningful in that the data included written language text produced by foreign learners, not native speakers.

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