  In this paper, we focus on data augmentation for the extreme multi-label
classification (XMC) problem. One of the most challenging issues of XMC is the
long tail label distribution where even strong models suffer from insufficient
supervision. To mitigate such label bias, we propose a simple and effective
augmentation framework and a new state-of-the-art classifier. Our augmentation
framework takes advantage of the pre-trained GPT-2 model to generate
label-invariant perturbations of the input texts to augment the existing
training data. As a result, it present substantial improvements over baseline
models. Our contributions are two-factored: (1) we introduce a new
state-of-the-art classifier that uses label attention with RoBERTa and combine
it with our augmentation framework for further improvement; (2) we present a
broad study on how effective are different augmentation methods in the XMC
task.
