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The few-shot natural language understanding (NLU) task has attracted muchrecent attention. However, prior methods have been evaluated under a disparateset of protocols, which hinders fair comparison and measuring progress of thefield. To address this issue, we introduce an evaluation framework thatimproves previous evaluation procedures in three key aspects, i.e., testperformance, dev-test correlation, and stability. Under this new evaluationframework, we re-evaluate several state-of-the-art few-shot methods for NLUtasks. Our framework reveals new insights: (1) both the absolute performanceand relative gap of the methods were not accurately estimated in priorliterature; (2) no single method dominates most tasks with consistentperformance; (3) improvements of some methods diminish with a larger pretrainedmodel; and (4) gains from different methods are often complementary and thebest combined model performs close to a strong fully-supervised baseline. Weopen-source our toolkit, FewNLU, that implements our evaluation framework alongwith a number of state-of-the-art methods.

FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding