利用
计算机科学
任务(项目管理)
建筑
质量(理念)
对话框
领域(数学分析)
医疗保健
领域知识
生成语法
人工智能
万维网
计算机安全
认识论
数学分析
哲学
艺术
视觉艺术
经济
经济增长
管理
数学
作者
Yanghui Li,Guihua Wen,Yang Hu,Mingnan Luo,Baochao Fan,Changjun Wang,Pei Yang
标识
DOI:10.1016/j.jbi.2021.103727
摘要
Online healthcare consultation offers people a convenient way to consult doctors. In this paper, we aim at building a generative dialog system for Chinese healthcare consultation. As the original Seq2seq architecture tends to suffer the issue of generating low-quality responses, the multi-source Seq2seq architecture generating more informative responses is much more preferred in this task. The multi-source Seq2seq architecture takes advantage of retrieval techniques to obtain responses from the database, and then takes these responses alongside the user-issued question as input. However, some of the retrieved responses might be not much related to the user-issued question, resulting in the generation of unsatisfying responses that are not correct in diagnosis or instead provide inappropriate advice on prevention or treatment. Therefore, this paper proposes multi-source Seq2seq guided by knowledge (MSSGK) to handle this problem. MSSGK differs from the multi-source Seq2seq architecture in that domain knowledge, including disease labels and topic labels about prevention and treatment, is introduced into the response generation via a multi-task learning framework. To better exploit the domain knowledge, we propose three attention mechanisms to provide more appropriate guidance for response generation. Experimental results on a dataset of real-world healthcare consultation show the effectiveness of the proposed method.
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