  Fine-tuning pretrained self-supervised language models is widely adopted for
transfer learning to downstream tasks. Fine-tuning can be achieved by freezing
gradients of the pretrained network and only updating gradients of a newly
added classification layer, or by performing gradient updates on all
parameters. Gradual unfreezing makes a trade-off between the two by gradually
unfreezing gradients of whole layers during training. This has been an
effective strategy to trade-off between storage and training speed with
generalization performance. However, it is not clear whether gradually
unfreezing layers throughout training is optimal, compared to sparse variants
of gradual unfreezing which may improve fine-tuning performance. In this paper,
we propose to stochastically mask gradients to regularize pretrained language
models for improving overall fine-tuned performance. We introduce GradDrop and
variants thereof, a class of gradient sparsification methods that mask
gradients during the backward pass, acting as gradient noise. GradDrop is
sparse and stochastic unlike gradual freezing. Extensive experiments on the
multilingual XGLUE benchmark with XLMR-Large show that GradDrop is competitive
against methods that use additional translated data for intermediate
pretraining and outperforms standard fine-tuning and gradual unfreezing. A
post-analysis shows how GradDrop improves performance with languages it was not
trained on, such as under-resourced languages.
