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

Written by Jason Wei on June 11, 2020.

I did this paper from start to finish in about five weeks. I was only 20 at the time—not even old enough to have a beer in the US—and frankly, I had not a clue how a transformer worked.

But by most metrics, this paper is pretty good. It was accepted to EMNLP with three positive reviews, and at the time that I write this addendum, it has more than 50 citations and 600+ stars on Github. Moreover, in the highlight of my career so far, an admitted student I met at the MLT Open House at CMU this spring asked if I wrote “the EDA paper” and if I went to “Brown (or was it UPenn?),” and then proceeded to call my work “kind-of a famous paper.”

I have talked to scores of people about these techniques at conferences, interviews, and open house days, and yet I still do not know why no one tried them before me (the closest I know of is “Robust Training under Linguistic Adversity” by Li et al., though they do not use the word “augment” in their paper). (If anyone knows, please send me a message.) Hopefully they can be useful for your research. If not, I hope this paper can at least be helpful to the aspiring researcher in a different way, perhaps best explained by Stephen King in his bestseller on creative writing, On Writing (apologies for his crudeness).

“Most writers can remember the first book he/she put down thinking: I can do better than this. Hell, I am doing better than this. What could be more encouraging to the struggling writer than to realize his/her work is unquestionably better than that of someone who actually got paid for his/her stuff?”