  Medical term normalization consists in mapping a piece of text to a large
number of output classes. Given the small size of the annotated datasets and
the extremely long tail distribution of the concepts, it is of utmost
importance to develop models that are capable to generalize to scarce or unseen
concepts. An important attribute of most target ontologies is their
hierarchical structure. In this paper we introduce a simple and effective
learning strategy that leverages such information to enhance the
generalizability of both discriminative and generative models. The evaluation
shows that the proposed strategy produces state-of-the-art performance on seen
concepts and consistent improvements on unseen ones, allowing also for
efficient zero-shot knowledge transfer across text typologies and datasets.
