Data Augmentation For Chinese Text Classification Using Back-Translation

Abstract

Abstract Text classification is a basic task in natural language processing. When the amount of data is insufficient, the classification accuracy will be greatly affected. We propose to use the back-translation method to expand three Chinese data sets used for text classification, and then train and predict the data sets through deep learning classification model. The results prove that using back-translation to expand the data is particularly helpful on a smaller dataset, it also can reduce the unbalanced distribution of samples and improve the classification performance.

Topics & Concepts