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 # EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
 This is the code for the ICLR Workshop paper [EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks.](https://arxiv.org/abs/1901.11196) A blog post that explains EDA is [here](https://medium.com/@jason.20/these-are-the-easiest-data-augmentation-techniques-in-natural-language-processing-you-can-think-of-88e393fd610). 
 
-By [Jason Wei](https://jasonwei20.github.io/research/) with Protago Labs AI Research.
+By [Jason Wei](https://jasonwei20.github.io/research/) and Kai Zou, with Protago Labs AI Research.
 
 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: