TextCNN-based ensemble learning model for Japanese Text Multi-classification

In this paper, we aim at improving Japanese text classification using TextCNN-based ensemble learning model. Specifically, we first construct three different sub-classifiers, combining ALBERT, RoBERTa, DistilBERT with TextCNN, respectively; and then explore the effectiveness of ensemble learning model to leverage complementary information from different sub-classifiers for better text classification. We also conduct a series of experiments with the dataset collected from Japanese Wikipedia pages, which was divided into 31 categories. The experimental results show that the proposed approach achieves a good performance. The accuracy, precision, recall and F1 scores reach 0.881, 0.884, 0.880 and 0.881, respectively, which shows that the TextCNN-based ensemble learning model can be used for Japanese Text Multi-Classification effectively.