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Found 3 papers, 2 papers with code
Enhancing Effectiveness and Robustness in a Low-Resource Regime via Decision-Boundary-aware Data Augmentation
Inspired by recent studies on decision boundaries, this paper proposes a decision-boundary-aware data augmentation strategy to enhance robustness using pretrained language models.
SoftEDA: Rethinking Rule-Based Data Augmentation with Soft Labels
Rule-based text data augmentation is widely used for NLP tasks due to its simplicity.
AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes
Text data augmentation is a complex problem due to the discrete nature of sentences.
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