Chinese Medical Question Answer Matching with Stack-CNN

Question and answer matching in Chinese medical science is a challenging problem, which requires an effective text semantic representation. In recent years, deep learning has achieved brilliant achievements in natural language processing field, which is utilized to capture various semantic features. In this paper, we propose a neural network, i.e., stack-CNN, to address question answer matching, which stacks multiple convolutional neural networks to capture the high-level semantic information from the low-level n-gram features. Substantial experiments on a real-world dataset show that our proposed model significantly outperforms a variety of strong baselines.