  Text classification tends to be difficult when data are deficient or when it
is required to adapt to unseen classes. In such challenging scenarios, recent
studies have often used meta-learning to simulate the few-shot task, thus
negating implicit common linguistic features across tasks. This paper addresses
such problems using meta-learning and unsupervised language models. Our
approach is based on the insight that having a good generalization from a few
examples relies on both a generic model initialization and an effective
strategy for adapting this model to newly arising tasks. We show that our
approach is not only simple but also produces a state-of-the-art performance on
a well-studied sentiment classification dataset. It can thus be further
suggested that pretraining could be a promising solution for few-shot learning
of many other NLP tasks. The code and the dataset to replicate the experiments
are made available at https://github.com/zxlzr/FewShotNLP.
