  Modern natural language understanding models depend on pretrained subword
embeddings, but applications may need to reason about words that were never or
rarely seen during pretraining. We show that examples that depend critically on
a rarer word are more challenging for natural language inference models. Then
we explore how a model could learn to use definitions, provided in natural
text, to overcome this handicap. Our model's understanding of a definition is
usually weaker than a well-modeled word embedding, but it recovers most of the
performance gap from using a completely untrained word.
