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Few-shot relation classification seeks to classify incoming query instancesafter meeting only few support instances. This ability is gained by trainingwith large amount of in-domain annotated data. In this paper, we tackle an evenharder problem by further limiting the amount of data available at trainingtime. We propose a few-shot learning framework for relation classification,which is particularly powerful when the training data is very small. In thisframework, models not only strive to classify query instances, but also seekunderlying knowledge about the support instances to obtain better instancerepresentations. The framework also includes a method for aggregatingcross-domain knowledge into models by open-source task enrichment.Additionally, we construct a brand new dataset: the TinyRel-CM dataset, afew-shot relation classification dataset in health domain with purposely smalltraining data and challenging relation classes. Experimental resultsdemonstrate that our framework brings performance gains for most underlyingclassification models, outperforms the state-of-the-art results given smalltraining data, and achieves competitive results with sufficiently largetraining data.

MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data