  The advent of large pre-trained language models has given rise to rapid
progress in the field of Natural Language Processing (NLP). While the
performance of these models on standard benchmarks has scaled with size,
compression techniques such as knowledge distillation have been key in making
them practical. We present, MATE-KD, a novel text-based adversarial training
algorithm which improves the performance of knowledge distillation. MATE-KD
first trains a masked language model based generator to perturb text by
maximizing the divergence between teacher and student logits. Then using
knowledge distillation a student is trained on both the original and the
perturbed training samples. We evaluate our algorithm, using BERT-based models,
on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive
adversarial learning and data augmentation baselines. On the GLUE test set our
6 layer RoBERTa based model outperforms BERT-Large.
