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State of the art models using deep neural networks have become very good inlearning an accurate mapping from inputs to outputs. However, they still lackgeneralization capabilities in conditions that differ from the ones encounteredduring training. This is even more challenging in specialized, and knowledgeintensive domains, where training data is limited. To address this gap, weintroduce MedNLI - a dataset annotated by doctors, performing a naturallanguage inference task (NLI), grounded in the medical history of patients. Wepresent strategies to: 1) leverage transfer learning using datasets from theopen domain, (e.g. SNLI) and 2) incorporate domain knowledge from external dataand lexical sources (e.g. medical terminologies). Our results demonstrateperformance gains using both strategies.

Lessons from Natural Language Inference in the Clinical Domain