Natural Language inference is the task of identifying relation between two sentences as entailment, contradiction or neutrality. MedNLI is a biomedical flavour of NLI for clinical domain. This paper explores the use of Bidirectional Encoder Representation from Transformer (BERT) for solving MedNLI. The proposed model, BERT pre-trained on PMC, PubMed and fine-tuned on MIMIC-III v1.4, achieves state of the art results on MedNLI (83.45%) and an accuracy of 78.5% in MEDIQA challenge. The authors present an analysis of the attention patterns that emerged as a result of training BERT on MedNLI using a visualization tool, bertviz. * *Equal Contribution: Kamal had sole access to MIMIC and MEDIQA data, focussed on the algorithm development and implementation. Suriyadeepan and Archana focussed on the attention visualisation and writing. Soham and Malaikannan focussed on reviewing
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