论文标题
这个患者有什么疾病？一个大规模的开放域问题，从体检中回答数据集
What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
论文作者
论文摘要
开放域问答（OPENQA）任务最近引起了自然语言处理（NLP）社区的越来越多的关注。在这项工作中，我们介绍了第一个自由形式的多项选择OpenQA数据集，用于解决医疗问题，从专业医疗委员会考试中收集的MEDQA。它涵盖了三种语言：英语，简化的中文和传统中文，并包含12,723、34,251和14,123个问题，分别为三种语言。我们通过顺序组合文档回收者和机器理解模型来实现基于规则和流行的神经方法。通过实验，我们发现即使是当前的最佳方法也只能达到36.7 \％，42.0 \％和70.1％的测试准确性，分别在英语，传统中文和简化的中文问题上。我们希望MEDQA将对现有的OpenQA系统提出巨大的挑战，并希望它可以作为一个平台，以促进未来NLP社区更强大的OpenQA模型。
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.
