Reading and understanding text is one important component in computer aided
diagnosis in clinical medicine, also being a major research problem in the
field of NLP. In this work, we introduce a question-answering task called MedQA
to study answering questions in clinical medicine using knowledge in a
large-scale document collection. The aim of MedQA is to answer real-world
questions with large-scale reading comprehension. We propose our solution
SeaReader--a modular end-to-end reading comprehension model based on LSTM
networks and dual-path attention architecture. The novel dual-path attention
models information flow from two perspectives and has the ability to
simultaneously read individual documents and integrate information across
multiple documents. In experiments our SeaReader achieved a large increase in
accuracy on MedQA over competing models. Additionally, we develop a series of
novel techniques to demonstrate the interpretation of the question answering
process in SeaReader.

该研究提出了一个名为 MedQA 的问答任务，使用大规模文档集中的知识研究临床医学中的问题解答。提出了一种基于 LSTM 网络和双路径注意力架构的模块化端到端阅读理解模型 SeaReader，其具有从两个角度建模信息流和同时读取和整合多篇文档的能力。实验证明，SeaReader 在 MedQA 上比竞争模型实现了大幅提高，同时开发了一系列新技术以展示 SeaReader 问题解答过程的解释。