关键词: 智能问诊, 反问生成, 文本循环神经网络, 双向变形编码器, 开源神经机器翻译

Abstract:

In order to improve the efficiency of doctor-patient dialogue by enabling doctors to quickly propose reasonable rhetorical questions in intelligent consultation， a method of rhetorical question generation based on deep neural network was proposed. Firstly， a large number of doctor-patient dialogue texts were obtained and labeled. Then， two classification models， Text Recurrent Neural Network （TextRNN） and Text Convolutional Neural Network （TextCNN）， were used to classify doctor’s statements respectively. Then， Text Recurrent Neural Network-Bidirectional Long Short-Term Memory （TextRNN-B） and Bidirectional Encoder Representations from Transformers （BERT） classification models were used to trigger questions. Six different Q&A selection methods were designed to simulate the situations in the field of medical consultation. Then， Open-Source Neural Machine Translation （OpenNMT） model was used to generate rhetorical questions. Finally， the generated rhetorical questions were evaluated comprehensively. Experimental results show that TextRNN is better than TextCNN in classification， and BERT model is better than TextRNN-B in question triggering； when OpenNMT model is used to realize rhetorical question generation in Window-top mode， the best results are obtained by using two evaluation indexes： Bilingual Evaluation Understudy （BLEU） and Perplexity （PPL）. The proposed method verifies the effectiveness of deep neural network technology in the generation of rhetorical questions， which can effectively solve the problem of doctor-patient question generation.

Key words: intelligent consultation, rhetorical question generation, Text Recurrent Neural Network (TextRNN), Bidirectional Encoder Representations from Transformers (BERT), Open-Source Neural Machine Translation (OpenNMT)