Hierarchical Data Augmentation and the Application in Text Classification

The applications of data augmentation in natural language processing have been limited. In this paper, we propose a novel method named Hierarchical Data Augmentation (HDA) which applied for text classification. Firstly, inspired by the hierarchical structure of texts, as words form a sentence and sentences form a document, HDA implements a hierarchical data augmentation strategy by augmenting texts at word-level and sentence level respectively. Secondly, inspired by the cropping, a popular method of data augmentation in computer vision, at each augmenting level, HDA utilizes attention mechanism to distill (crop) important contents from texts hierarchically as summaries of texts. Specifically, we use a trained Hierarchical Attention Networks (HAN) model to obtain attention values of all documents in training sets at both levels respectively, which are further used to extract the most important part of words/sentences and generate new samples by concatenating them in order. Then we gain two levels of augmented datasets, WordSet and SentSet. Finally, extending training set with certain amount of HDA-generated samples and we evaluate models' performance with new training set. The results reveal HDA can generate massive and high-quality augmented samples at both levels, and models using these samples can obtain significant improvements. Compared with the existing methods, HDA enjoys the simplicity both on theory and implementation, and it can augment texts at two levels for the diversity of data.