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Machine Reading Comprehension (MRC) aims to extract answers to questionsgiven a passage. It has been widely studied recently, especially in opendomains. However, few efforts have been made on closed-domain MRC, mainly dueto the lack of large-scale training data. In this paper, we introduce amulti-target MRC task for the medical domain, whose goal is to predict answersto medical questions and the corresponding support sentences from medicalinformation sources simultaneously, in order to ensure the high reliability ofmedical knowledge serving. A high-quality dataset is manually constructed forthe purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), withdetailed analysis conducted. We further propose the Chinese medical BERT modelfor the task (CMedBERT), which fuses medical knowledge into pre-trainedlanguage models by the dynamic fusion mechanism of heterogeneous features andthe multi-task learning strategy. Experiments show that CMedBERT consistentlyoutperforms strong baselines by fusing context-aware and knowledge-aware tokenrepresentations.

Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources