Text Augmentation Using Dataset Reconstruction for Low-Resource Classification

Abstract

In the deployment of real-world text clas- sification models, label scarcity is a com- mon 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 in- volves using the text-generation capabili- ties of language models. We propose Text AUgmentation by Dataset Reconstruction (TAU-DR), a novel method of data aug- mentation for text classification. We con- duct experiments on several multi-class datasets, showing that our approach im- proves the current state-of-the-art tech- niques for data augmentation.