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

• URL: http://arxiv.org/abs/2009.13081v1
• Date: Mon, 28 Sep 2020 05:07:51 GMT
• Title: What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
• Authors: Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang and Peter Szolovits
• Abstract summary: We present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively.
• Score: 35.644831813174974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7\%, 42.0\%, and 70.1\% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

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