Serial Number: https://www.earticle.net/Article/A393855
Title: Automatic Score Range Classification of Korean Essays Using Deep Learning-based Korean Language Models - The Case of KoBERT & KoGPT2 -
Author: Cho Heeryeon, Yi Yumi, Im Hyeonyeol, Cha Junwoo, Lee Chankyu
Journal: Journal of the International Network for Korean Language and Culture
Vol: 18(1)
Pages: 217-241
Date: 2021.
Register Information: KCI
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<Abstract>
Automatic Score Range Classification of Korean Essays Using Deep Learning-based Korean Language Models-The Case of KoBERT & KoGPT2-. We investigate the performance of deep learning-based Korean language models on a task of automatically classifying Korean essays written by foreign students. We construct an experimental data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job (‘job’), conditions of a happy life (‘happiness’), relationship between money and happiness, and definition of success. These essays were divided into four scoring levels, and using this 4-class data set, we fine-tuned two Korean deep learning-based language models, namely, KoBERT and KoGPT2, to use them in the automatic essay classification experiment. The 7-fold cross validation classification accuracies of ‘job’ and ‘happiness’ essays were 48.8% and 65.2% respectively for KoBERT, and 50.6% and 58.9% respectively for KoGPT2. Furthermore, the 7-fold cross validation classification accuracies of the integrated dataset that combined all essays were 54.5% and 46.5% for KoBERT and KoGPT2 respectively.
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