Iterative Translation-Based Data Augmentation Method for Text Classification Tasks

Abstract

A well-known limitation of existing rule-based text augmentation is that it cannot be applied to other languages because it depends on grammatical and structural characteristics. Moreover, most text Generative Adversarial Networks (GAN) are unstable in training due to inefficient generator optimization and rely on maximum likelihood pre-training. This paper addresses the above problems by proposing a novel augmentation method with a Sentence Generator (SG) and Sentence Discriminator (SD) for Iterative Translation-based Data Augmentation (ITDA). This paper makes three original contributions. First, the ITDA SG is designed to provide universal multiple-language support by generating comprehensive augmented sentences through serial and parallel iterations of an existing translator, such as Google Translate. Second, given that the quality of the generated sentences varies depending on the translation combination or the type of sentence, the ITDA addresses this issue using a discriminator to achieve sentence augmentation, which can select high-quality augmented data using a text classifier. Third, the ITDA can perform sentence augmentation for 109 different languages using discriminators based on text classifiers trained for a specific language or type of data set. Extensive experiments are conducted to evaluate the efficacy of the ITDA using a Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), CNN-BiLSTM, and self-attention. The results demonstrate that when the ITDA is applied to 480 sentence classification tasks, the average accuracy increases by 4.24%.