  State of the art models using deep neural networks have become very good in
learning an accurate mapping from inputs to outputs. However, they still lack
generalization capabilities in conditions that differ from the ones encountered
during training. This is even more challenging in specialized, and knowledge
intensive domains, where training data is limited. To address this gap, we
introduce MedNLI - a dataset annotated by doctors, performing a natural
language inference task (NLI), grounded in the medical history of patients. We
present strategies to: 1) leverage transfer learning using datasets from the
open domain, (e.g. SNLI) and 2) incorporate domain knowledge from external data
and lexical sources (e.g. medical terminologies). Our results demonstrate
performance gains using both strategies.
