A Survey on Data Augmentation for Text Classification

Abstract

Data augmentation, the artificial creation of training data for machine learning by transformations, is a widely studied research field across machine learning disciplines. While it is useful for increasing a model's generalization capabilities, it can also address many other challenges and problems, from overcoming a limited amount of training data, to regularizing the objective, to limiting the amount data used to protect privacy. Based on a precise description of the goals and applications of data augmentation and a taxonomy for existing works, this survey is concerned with data augmentation methods for textual classification and aims to provide a concise and comprehensive overview for researchers and practitioners. Derived from the taxonomy, we divide more than 100 methods into 12 different groupings and give state-of-the-art references expounding which methods are highly promising by relating them to each other. Finally, research perspectives that may constitute a building block for future work are provided.

44 pages, 5 figures, 9 tables

References in corpus (20)

• Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
• Language Models are Few-Shot Learners
• AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
• Assessing BERT's Syntactic Abilities
• CLEAR: Contrastive Learning for Sentence Representation
• Data Noising as Smoothing in Neural Network Language Models
• Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers
• Augmenting Data with Mixup for Sentence Classification: An Empirical Study
• What do you learn from context? Probing for sentence structure in contextualized word representations
• A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation
• Adversarial Training for Large Neural Language Models
• Learning Data Manipulation for Augmentation and Weighting
• Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation
• Soft Contextual Data Augmentation for Neural Machine Translation
• Neural Data Augmentation via Example Extrapolation
• CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding
• AugLy: Data Augmentations for Robustness
• NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
• MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification
• Empirical Study of Text Augmentation on Social Media Text in Vietnamese

Cited by in corpus (10)

• Data Augmentation Approaches in Natural Language Processing: A Survey
• Data Augmentation in Natural Language Processing: A Novel Text Generation Approach for Long and Short Text Classifiers
• Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders
• Explainable AI: XAI-Guided Context-Aware Data Augmentation
• Gender Bias Detection in Court Decisions: A Brazilian Case Study
• Evaluating the Impact of Data Augmentation on Predictive Model Performance
• AI Simulation by Digital Twins: Systematic Survey, Reference Framework, and Mapping to a Standardized Architecture
• Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification
• Revisiting data augmentation for subspace clustering
• EmoGRACE: Aspect-based emotion analysis for social media data