Co-attention fusion based deep neural network for Chinese medical answer selection

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.