Medical Exam Question Answering with Large-scale Reading Comprehension
This work addresses the challenge of computer-aided diagnosis in clinical medicine by improving question-answering accuracy, though it appears incremental as it builds on existing reading comprehension methods.
The authors tackled the problem of answering clinical medicine questions using large-scale document collections, introducing the MedQA task and proposing SeaReader, a modular end-to-end reading comprehension model based on LSTM networks and dual-path attention, which achieved a large increase in accuracy over competing models.
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.