@inproceedings{wei-etal-2018-task,
title = "Task-oriented Dialogue System for Automatic Diagnosis",
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",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-2033",
doi = "10.18653/v1/P18-2033",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Task-oriented Dialogue System for Automatic Diagnosis
%A Wei, Zhongyu
%A Liu, Qianlong
%A Peng, Baolin
%A Tou, Huaixiao
%A Chen, Ting
%A Huang, Xuanjing
%A Wong, Kam-fai
%A Dai, Xiangying
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F wei-etal-2018-task
%X 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.
%R 10.18653/v1/P18-2033
%U https://aclanthology.org/P18-2033
%U https://doi.org/10.18653/v1/P18-2033
%P 201-207
Markdown (Informal)
[Task-oriented Dialogue System for Automatic Diagnosis](https://aclanthology.org/P18-2033) (Wei et al., ACL 2018)
ACL
- Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, and Xiangying Dai. 2018. Task-oriented Dialogue System for Automatic Diagnosis. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 201–207, Melbourne, Australia. Association for Computational Linguistics.