On the Generation of Medical Question-Answer Pairs


  • Sheng Shen University of California, Berkeley
  • Yaliang Li Alibaba Group
  • Nan Du Tencent
  • Xian Wu Tencent
  • Yusheng Xie Tencent
  • Shen Ge Tencent
  • Tao Yang Tencent
  • Kai Wang Tencent
  • Xingzheng Liang Tencent
  • Wei Fan Tencent




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. 1




How to Cite

Shen, S., Li, Y., Du, N., Wu, X., Xie, Y., Ge, S., Yang, T., Wang, K., Liang, X., & Fan, W. (2020). On the Generation of Medical Question-Answer Pairs. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8822-8829. https://doi.org/10.1609/aaai.v34i05.6410



AAAI Technical Track: Natural Language Processing