Document-based Question Answering tries to rank the candidate answers for given questions, which needs to evaluate matching score between the question sentence and answer sentence. Existing works usually utilize convolution neural network (CNN) to adaptively learn the latent matching pattern between the question/answer pair. However, CNN can only perceive the order of a word in a local windows, while the global order of the windows is ignored due to the window-sliding operation. In this report, we design an enhanced CNN (https://github.com/ shuishen112/pairwise-deep-qa) with extended order information (e.g. overlapping position and global order) into inputting embedding, such rich representation makes it possible to learn an order-aware matching in CNN. Combining with standard convolutional paradigm like attentive pooling, pair-wise training and dynamic negative sample, this end-to-end CNN achieve a good performance on the DBQA task of NLPCC 2017 without any other extra features.
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