Chinese selection is one of the most important subtasks in Chinese medical question-answer system. To obtain the representations of question and answer, an attractive method is to use the attentive pooling based deep neural network. However, this method suffers from the over-pooling problem. It generates attentive information by only using the related medical keywords, and neglects the local semantic information of sentences. In this paper, a novel co-attention fusion based deep neural network method is proposed. Our method solves the over-pooling problem by fusing local semantic information with attentive information. Because of the usage of the fusion mechanism, the proposed method tends to obtain more useful information for pooling and produce better representations for question and answer. For comparison, we create a new Chinese medical answer selection dataset in the epilepsy theme (i.e., cEpilepsyQA), and our method performs much better than the state-of-the-art methods. Also, the proposed method gets competitive results on the public Chinese medical answer selection datasets: cMedQA v1.0 and v2.0.

中文选择是中医问答系统中最重要的子任务之一。为了获得问题和答案的表示，一种有吸引力的方法是使用基于注意力集中的深度神经网络。但是，这种方法存在过度合并问题。它仅通过使用相关的医学关键字来生成注意信息，而忽略了句子的局部语义信息。本文提出了一种基于共注意力融合的新型深度神经网络方法。我们的方法通过将局部语义信息与注意信息融合来解决超池问题。由于融合机制的使用，所提出的方法趋于获得更多有用的信息进行合并，并产生更好的表示问题和回答的方式。为了比较，我们以癫痫为主题创建了一个新的中医答案选择数据集（即cEpilepsyQA），并且我们的方法比最新方法的性能要好得多。而且，该方法在公开的中医答案选择数据集：cMedQA v1.0和v2.0中获得了竞争性结果。