Data augmentation methods for classifying Korean texts

Data augmentation is widely adopted in computer vision.In contrast, research on data augmentation in the field of natural language processing has been limited.We propose several data augmentation methods to support the classification of Korean texts.We increase the size and diversity of text data which are specifically tailored to Korean.These methods adopt and adjust the existing data augmentation for English texts.We could improve the classification accuracy and sometimes regularize the natural language models to reduce the overfits.Our contribution to the data augmentation regarding Korean texts compose of three parts.1) data augmentation with Spelling Correction, 2) Easy data augmentation based on part-of-speech tagging, and 3) Data augmentation with conditional Masked Language Modeling.Our experiments show that classification accuracy can be improved with the aids of our proposed methods.Due to the limit of computing facilities, we consider rather small-scale Korean texts only.