Concept Matching for Low-Resource Classification

In many applications that rely on machine learning, the availability of labelled data is a matter of primary importance. However, when tackling new tasks, labels are usually missing and must be collected from scratch by the users. In this work, we address the problem of learning classifiers when the amount of labels is very scarce. We do so by learning multiple vectors, called prototypes, that rep...