Text Augmentation Using Dataset Reconstruction for Low-Resource Classification

Abstract

In the deployment of real-world text classification models, label scarcity is a common problem. As the number of classes increases, this problem becomes even more complex. One way to address this problem is by applying text augmentation methods. One of the more prominent methods involves using the text-generation capabilities of language models. We propose Text AUgmentation by Dataset Reconstruction (TAU-DR), a novel method of data augmentation for text classification. We conduct experiments on several multi-class datasets, showing that our approach improves the current state-of-the-art techniques for data augmentation.