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In this paper, we present Biomedical MultiTask Deep Neural Network (Bio-MTDNN) on the NLI task of MediQA 2019 challenge (Ben Abacha et al., 2019). Bio-MTDNN utilizes ”transfer learning” based paradigm where not only the source and target domains are different but also the source and target tasks are varied, although related. Further, Bio-MTDNN integrates knowledge from external sources such as clinical databases (UMLS) enhancing its performance on the clinical domain. Our proposed method outperformed the official baseline and other prior models (such as ESIM and Infersent on dev set) by a considerable margin as evident from our experimental results.

MSIT_SRIB at MEDIQA 2019: Knowledge Directed Multi-task Framework for Natural Language Inference in Clinical Domain.