Data augmentation techniques in natural language processing

Abstract

Data Augmentation (DA) methods – a family of techniques designed for synthetic generation of training data – have shown remarkable results in various Deep Learning and Machine Learning tasks. Despite its widespread and successful adoption within the computer vision community, DA techniques designed for natural language processing (NLP) tasks have exhibited much slower advances and limited success in achieving performance gains. As a consequence, with the exception of applications of back-translation to machine translation tasks, these techniques have not been as thoroughly explored by the wider NLP community. Recent research on the subject still lacks a proper practical understanding of the relationship between the various existing DA methods. The connection between DA methods and several important aspects of its outputs, such as lexical diversity and semantic fidelity, is also still poorly understood. In this work, we perform a comprehensive study of NLP DA techniques, comparing their relative performance under different settings. We analyze the quality of the synthetic data generated, evaluate its performance gains and compare all of these aspects to previous existing DA procedures.