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Self-supervised Data Augmentation for Text Classification in Low-Data Settings

Due to data sparsity and high annotation cost, data augmentation has established itself as an effective tool for boosting model performance on supervised NLP tasks. Where task-agnostic augmentation methods tend to act as simple regularizers for the data, task-aware methods also leverage labels for the generation of data that are most suitable for downstream tasks. While prior work has investigated generation and sampling strategies individually, the potential of a self-supervised approach that leverages multiple pre-trained models in generation and sampling remains underexplored. To address this issue, we present an ensemble-based framework of language models that proposes augmentation candidates and internally reviews their suitability for low-resource text classification tasks. We evaluate our model on six classification benchmarks and find that it consistently outperforms state-of-the-art data augmentation baselines in classification accuracy by an average of 0.97 points in low-data scenarios.