  Pretrained language models (PLMs) have demonstrated remarkable performance in
various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are
well known for their superior text generation capabilities; bidirectional PLMs
(e.g., BERT) have been the prominent choice for natural language understanding
(NLU) tasks. While both types of models have achieved promising few-shot
learning performance, their potential for zero-shot learning has been
underexplored. In this paper, we present a simple approach that uses both types
of PLMs for fully zero-shot learning of NLU tasks without requiring any
task-specific data: A unidirectional PLM generates class-conditioned texts
guided by prompts, which are used as the training data for fine-tuning a
bidirectional PLM. With quality training data selected based on the generation
probability and regularization techniques (label smoothing and temporal
ensembling) applied to the fine-tuning stage for better generalization and
stability, our approach demonstrates strong performance across seven
classification tasks of the GLUE benchmark (e.g., 72.3/73.8 on MNLI-m/mm and
92.8 on SST-2), significantly outperforming zero-shot prompting methods and
achieving even comparable results to strong few-shot approaches using 32
training samples per class.
