  Data augmentation has been widely used in low-resource NER tasks to tackle
the problem of data sparsity. However, previous data augmentation methods have
the disadvantages of disrupted syntactic structures, token-label mismatch, and
requirement for external knowledge or manual effort. To address these issues,
we propose Robust Prompt-based Data Augmentation (RoPDA) for low-resource NER.
Based on pre-trained language models (PLMs) with continuous prompt, RoPDA
performs entity augmentation and context augmentation through five fundamental
augmentation operations to generate label-flipping and label-preserving
examples. To optimize the utilization of the augmented samples, we present two
techniques: Self-Consistency Filtering and mixup. The former effectively
eliminates low-quality samples, while the latter prevents performance
degradation arising from the direct utilization of label-flipping samples.
Extensive experiments on three benchmarks from different domains demonstrate
that RoPDA significantly improves upon strong baselines, and also outperforms
state-of-the-art semi-supervised learning methods when unlabeled data is
included.
