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

Understand

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.