  Knowledge distillation is one of the primary methods of transferring
knowledge from large to small models. However, it requires massive
task-specific data, which may not be plausible in many real-world applications.
Data augmentation methods such as representation interpolation, token
replacement, or augmentation with models are applied to tackle this problem.
However, these data augmentation methods either potentially cause shifts in
decision boundaries (representation interpolation), are not expressive enough
(token replacement), or introduce too much computational overhead (augmentation
with models). To this end, we propose AugPro (Augmentation with Projection), an
effective and efficient data augmentation method for distillation. Our method
builds on top of representation interpolation augmentation methods to maintain
the diversity of expressions and converts the augmented data to tokens to avoid
shifting decision boundaries. It uses simple operations that come with little
computational overhead. The results on multiple GLUE tasks show that our
methods can improve distillation performance by a large margin at a low time
cost. Codes are available at
https://github.com/google-research/google-research/tree/master/augpro.
