  We present EDA: easy data augmentation techniques for boosting performance on
text classification tasks. EDA consists of four simple but powerful operations:
synonym replacement, random insertion, random swap, and random deletion. On
five text classification tasks, we show that EDA improves performance for both
convolutional and recurrent neural networks. EDA demonstrates particularly
strong results for smaller datasets; on average, across five datasets, training
with EDA while using only 50% of the available training set achieved the same
accuracy as normal training with all available data. We also performed
extensive ablation studies and suggest parameters for practical use.
