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

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks