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Long text brings a big challenge to semantic matching due to theircomplicated semantic and syntactic structures. To tackle the challenge, weconsider using prior knowledge to help identify useful information and filterout noise to matching in long text. To this end, we propose a knowledgeenhanced hybrid neural network (KEHNN). The model fuses prior knowledge intoword representations by knowledge gates and establishes three matching channelswith words, sequential structures of sentences given by Gated Recurrent Units(GRU), and knowledge enhanced representations. The three channels are processedby a convolutional neural network to generate high level features for matching,and the features are synthesized as a matching score by a multilayerperceptron. The model extends the existing methods by conducting matching onwords, local structures of sentences, and global context of sentences.Evaluation results from extensive experiments on public data sets for questionanswering and conversation show that KEHNN can significantly outperformthe-state-of-the-art matching models and particularly improve the performanceon pairs with long text.

Knowledge Enhanced Hybrid Neural Network for Text Matching