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Reading and understanding text is one important component in computer aideddiagnosis in clinical medicine, also being a major research problem in thefield of NLP. In this work, we introduce a question-answering task called MedQAto study answering questions in clinical medicine using knowledge in alarge-scale document collection. The aim of MedQA is to answer real-worldquestions with large-scale reading comprehension. We propose our solutionSeaReader--a modular end-to-end reading comprehension model based on LSTMnetworks and dual-path attention architecture. The novel dual-path attentionmodels information flow from two perspectives and has the ability tosimultaneously read individual documents and integrate information acrossmultiple documents. In experiments our SeaReader achieved a large increase inaccuracy on MedQA over competing models. Additionally, we develop a series ofnovel techniques to demonstrate the interpretation of the question answeringprocess in SeaReader.

Medical Exam Question Answering with Large-scale Reading Comprehension