We present **EDA**: **e**asy **d**ata **a**ugmentation techniques for boosting performance on text classification tasks. These are a generalized set of data augmentation techniques that are easy to implement and have shown improvements on five NLP classification tasks, with substantial improvements on datasets of size *N<500*. While other techniques require you to train a language model on an external dataset just to get a small boost, we found that simple text editing operations using EDA result in substantial performance gains. Given a sentence in the training set, we perform the following operations:
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