Syntactically Coherent Text Augmentation for Sequence Classification

Abstract

In this article, we address the problem of data scarcity for the sequence classification tasks. We propose AugmentGAN, a simple-yet-effective generative adversarial network-based text augmentation model, which ensures syntactic coherency in the newly generated samples. Given an input with a label, AugmentGAN aims to generate a semantically similar sequence that follows the syntactic structure of the original sample. Exhaustive task-based evaluation is conducted to show the efficacy of AugmentGAN—we employ 12 different datasets across five classification tasks, i.e., sentiment analysis, emotion recognition, sarcasm detection, intent classification, and spam detection. We observe that, compared to the existing text augmentation techniques, AugmentGAN yields an improved performance across datasets for all the tasks. AugmentGAN also turns out to be effective for multiple languages, i.e., English, Hindi, and Bengali.