한국어 에세이 문항 자동 채점을 위한 딥러닝 알고리듬 탐색 = Deep Learning Algorithm Exploration for Automated Korean essay Scoring

다국어 초록 (Multilingual Abstract)

This study was carried out for the purpose of searching for the optimal algorithm for automated scoring system of Korean essay through the comparison of deep learning-based learning models. For this purpose, in this study, deep learning algorithms such as Recurrent Neural Network (RNN), Long-Short-Term-Memory (LSTM), and Gated-Recurrent-Unit (GRU) algorithms were compared. The performance of each algorithm was evaluated based on classification accuracy, precision, recall, and F1. The empirical results showed that the LSTM and GRU algorithm-based models performed better than RNN. Although there is no significant difference in model performance between LSTM and GRU, the GRU algorithm was found to be more efficient in terms of the time required to train the model, so it could be considered to be the optimal algorithm for automated scoring if the machine leanring time is critical.