  Question answering (QA) has achieved promising progress recently. However,
answering a question in real-world scenarios like the medical domain is still
challenging, due to the requirement of external knowledge and the insufficient
quantity of high-quality training data. In the light of these challenges, we
study the task of generating medical QA pairs in this paper. With the insight
that each medical question can be considered as a sample from the latent
distribution of questions given answers, we propose an automated medical QA
pair generation framework, consisting of an unsupervised key phrase detector
that explores unstructured material for validity, and a generator that involves
a multi-pass decoder to integrate structural knowledge for diversity. A series
of experiments have been conducted on a real-world dataset collected from the
National Medical Licensing Examination of China. Both automatic evaluation and
human annotation demonstrate the effectiveness of the proposed method. Further
investigation shows that, by incorporating the generated QA pairs for training,
significant improvement in terms of accuracy can be achieved for the
examination QA system.
