支持中文医疗问答的基于注意力机制的栈卷积神经网络模型

当前的中文问答匹配技术大多都需要先进行分词，中文医疗文本的分词问题需要维护医学词典来缓解分词错误对后续任务影响，而维护词典需要大量人力和知识，致使分词问题一直具有极大的挑战性。同时，现有的中文医疗问答匹配方法都是对问题和答案分开建模，并未考虑问题和答案中各自包含的关键词汇间的关联关系。因此，提出了一种基于注意力机制的栈卷积神经网络（Att-StackCNN）模型来解决中文医疗问答匹配问题。首先，使用字嵌入对问题和答案进行编码以得到二者各自的字嵌入矩阵；然后，通过利用问题和答案的字嵌入矩阵构造注意力矩阵来得到二者各自的特征注意力映射矩阵；接着，利用栈卷积神经网络（Stack-CNN）模型同时对上述矩阵进行卷积操作，从而得到问题和答案各自的语义表示；最后，进行相似度计算，并利用相似度计算最大边际损失以更新网络参数。所提模型在cMedQA数据集上的Top-1正确率比Stack-CNN模型高接近1个百分点，比Multi-CNNs模型高接近0.5个百分点。实验结果表明，Att-StackCNN模型可以提升中文医疗问答匹配效果。

Attention mechanism based Stack-CNN model to support Chinese medical questions and answers

Most of the current Chinese questions and answers matching technologies require word segmentation first， and the word segmentation problem of Chinese medical text requires maintenance of medical dictionaries to reduce the impact of segmentation errors on subsequent tasks. However， maintaining dictionaries requires a lot of manpower and knowledge， making word segmentation problem always be a great challenge. At the same time， the existing Chinese medical questions and answers matching methods all model the questions and the answers separately， and do not consider the relationship between the keywords contained in the questions and the answers respectively. Therefore， an Attention mechanism based Stack Convolutional Neural Network （Att-StackCNN） model was proposed to solve the problem of Chinese medical questions and answers matching. Firstly， character embedding was used to encode the questions and answers to obtain the respective character embedding matrices. Then， the respective feature attention mapping matrices were obtained by constructing the attention matrix using the character embedding matrices of the questions and answers. After that， Stack Convolutional Neural Network （Stack-CNN） model was used to perform convolution operation to the above matrices at the same time to obtain the respective semantic representations of the questions and answers. Finally， the similarity was calculated， and the max-margin loss was calculated by using the similarity to update the network parameters. On the cMedQA dataset， the Top-1 accuracy of proposed model was about 1 percentage point higher than that of Stack-CNN model and about 0.5 percentage point higher than that of Multi-CNNs model. Experimental results show that Att-StackCNN model can improve the matching effect of Chinese medical questions and answers.