【摘要】 中文医疗自动问答由于其语言和领域的特点，使其更具挑战性。为更好表示医疗问答句的语义信息，提出融合字词增强语义多注意力的医疗自动问答方法。首先，利用预训练模型BERT和WoBERT分别提取文本基于词级和字级的向量表示，并将两者融合，以获得句子向量更完善的语义信息；然后加入注意力机制产生包含问题信息的答案表示，输入到双向门控循环单元获取句子的全局语义特征；最后通过多注意力池化模块实现问题和答案之间相关性的专注交互，通过问答对相似度的计算，找出最匹配答案。在cMedQA医学数据集上的实验表明，论文所提方法与其他基于深度学习的方法相比，ACC@1有所提高，证明融合字词的向量表示与神经网络前加入注意力能够提升自动问答模型的性能。更多还原

【Abstract】 Chinese medical question answer are made more challenging by their language and domain specificity. To better represent the Sentence meaning of medical question answer,this paper proposes a method for fusing char-word to enhance semantics with multi-attentive for medical question answering. Firstly,the pre-trained models BERT and WordBERT are used to extract the vector representations of the text at the word level and character level respectively,and the two are fused to obtain more complete semantic information of the sentence vector. Then,an attention mechanism is added to generate an answer representation containing information about the question,which is input to bidirectional gating recurrent unit to obtain the overall semantic features of the sentence. Finally,the multi-attention pooling module enables the interaction of relevance between questions and answers,and finds the best matching answer by calculating the similarity of question-answer pairs. Experimental notes on the cMedQA medical dataset,the method in this paper has an improved on ACC@1 compared to other deep learning-based methods,it is demonstrated that fusing vector representations of char-words with the addition of attention in front of the neural network can improve the performance of automatic question answer models.更多还原