  Despite their recent successes in tackling many NLP tasks, large-scale
pre-trained language models do not perform as well in few-shot settings where
only a handful of training examples are available. To address this shortcoming,
we propose STraTA, which stands for Self-Training with Task Augmentation, an
approach that builds on two key ideas for effective leverage of unlabeled data.
First, STraTA uses task augmentation, a novel technique that synthesizes a
large amount of data for auxiliary-task fine-tuning from target-task unlabeled
texts. Second, STraTA performs self-training by further fine-tuning the strong
base model created by task augmentation on a broad distribution of
pseudo-labeled data. Our experiments demonstrate that STraTA can substantially
improve sample efficiency across 12 few-shot benchmarks. Remarkably, on the
SST-2 sentiment dataset, STraTA, with only 8 training examples per class,
achieves comparable results to standard fine-tuning with 67K training examples.
Our analyses reveal that task augmentation and self-training are both
complementary and independently effective.
