Unsupervised Data Augmentation (UDA) shows how supervised data augmentation (DA) techniques can be applied to unsupervised data through consistency training on $$(x, DA(x))$$ pairs.

University of Michigan - Ann Arbor

Google

Rule-based data augmentation is to apply basic changes to the dataset which enlarges the divergence that the neural network sees.

Rule-based Data Augmentation

https://arxiv-download.xixiaoyao.cn/pdf/2105.03075.pdf

A Survey of Data Augmentation Approaches for NLP

Feature space DA approaches generate augmented examples in the model’s feature space rather than input data.

Feature Space DA

It's a set of token-level random perturbation operations including random insertion, deletion, and swap. It shows improved performance on many text classification tasks. 

Easy Data Augmentation (EDA)

Unsupervised Data Augmentation (UDA)

For dependency-annotated sentences, children of the same parent are swapped or some deleted.

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