Machine Reading Comprehension (MRC) aims to extract answers to questions
given a passage. It has been widely studied recently, especially in open
domains. However, few efforts have been made on closed-domain MRC, mainly due
to the lack of large-scale training data. In this paper, we introduce a
multi-target MRC task for the medical domain, whose goal is to predict answers
to medical questions and the corresponding support sentences from medical
information sources simultaneously, in order to ensure the high reliability of
medical knowledge serving. A high-quality dataset is manually constructed for
the purpose, named Multi-task Chinese Medical MRC dataset (CMedMRC), with
detailed analysis conducted. We further propose the Chinese medical BERT model
for the task (CMedBERT), which fuses medical knowledge into pre-trained
language models by the dynamic fusion mechanism of heterogeneous features and
the multi-task learning strategy. Experiments show that CMedBERT consistently
outperforms strong baselines by fusing context-aware and knowledge-aware token
representations.

本论文介绍了一个多目标的医疗领域机器阅读理解任务，同时提出了一个高质量的医学数据集 CMedMRC，并提出了一种医疗 BERT 模型 CMedBERT，这种模型通过异构特征的动态融合和多任务学习策略将医学知识融入到预训练的语言模型中，实验表明 CMedBERT 通过融合上下文感知和知识感知的令牌表示，始终优于强基线。