  Long text brings a big challenge to semantic matching due to their
complicated semantic and syntactic structures. To tackle the challenge, we
consider using prior knowledge to help identify useful information and filter
out noise to matching in long text. To this end, we propose a knowledge
enhanced hybrid neural network (KEHNN). The model fuses prior knowledge into
word representations by knowledge gates and establishes three matching channels
with words, sequential structures of sentences given by Gated Recurrent Units
(GRU), and knowledge enhanced representations. The three channels are processed
by a convolutional neural network to generate high level features for matching,
and the features are synthesized as a matching score by a multilayer
perceptron. The model extends the existing methods by conducting matching on
words, local structures of sentences, and global context of sentences.
Evaluation results from extensive experiments on public data sets for question
answering and conversation show that KEHNN can significantly outperform
the-state-of-the-art matching models and particularly improve the performance
on pairs with long text.
