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Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus calleddoi:10.18653/v1/w19-5027 dblp:conf/bionlp/TianMXS19 fatcat:2jvznf4znjhq3gwbnykmblrt4u