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Open domain question answering (OpenQA) tasks have been recently attractingmore and more attention from the natural language processing (NLP) community.In this work, we present the first free-form multiple-choice OpenQA dataset forsolving medical problems, MedQA, collected from the professional medical boardexams. It covers three languages: English, simplified Chinese, and traditionalChinese, and contains 12,723, 34,251, and 14,123 questions for the threelanguages, respectively. We implement both rule-based and popular neuralmethods by sequentially combining a document retriever and a machinecomprehension model. Through experiments, we find that even the current bestmethod can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on theEnglish, traditional Chinese, and simplified Chinese questions, respectively.We expect MedQA to present great challenges to existing OpenQA systems and hopethat it can serve as a platform to promote much stronger OpenQA models from theNLP community in the future.

What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams