Real-world relation distributions are long-tail. This means only the common relations obtain sufficient training instances and most relations have very limited relational facts and corresponding sentences. Few-shot learning focuses on grasping tasks with only a few training examples and is a good fit for this need. The general idea of few-shot models is to train good representations of instances or learn ways of fast adaptation from existing large-scale data, and then transfer to new tasks.

Let T be some task, E be past experience related to this classes of task T, and P be performance measure of T. Few-Shot learning is a type of machine learning problem which is specified by E, T, and P, but only has limited E either quantitatively or qualitatively. E typically includes supervised information and prior knowledge.