@InProceedings{wei-EtAl:2018:Short,
  author    = {Wei, Zhongyu  and  Liu, Qianlong  and  Peng, Baolin  and  Tou, Huaixiao  and  Chen, Ting  and  Huang, Xuanjing  and  Wong, Kam-Fai  and  Dai, Xiangying},
  title     = {Task-oriented Dialogue System for Automatic Diagnosis},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {201--207},
  abstract  = {In this paper, we make a move to build a dialogue system for automatic diagnosis. We first build a dataset collected from an online medical forum by extracting symptoms from both patients' self-reports and conversational data between patients and doctors. Then we propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. Experimental results on our dataset show that additional symptoms extracted from conversation can greatly improve the accuracy for disease identification and our dialogue system is able to collect these symptoms automatically and make a better diagnosis.},
  url       = {http://www.aclweb.org/anthology/P18-2033}
}