Saama Research at MEDIQA 2019: Pre-trained BioBERT with Attention Visualisation for Medical Natural Language Inference

WS 2019  ·  Kamal raj Kanakarajan, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana, Soham Chatterjee, Malaikannan Sankarasubbu ·

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 MIMICIII 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. read more

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Natural Language Inference MedNLI BioBERT-MIMIC Accuracy 83.45 # 3

Methods