Gated Convolutional Neural Network for Sentence Matching

The recurrent neural networks (RNN) have shown promising results in sentence matching tasks, such as paraphrase identification (PI), natural language inference (NLI) and answer selection (AS). However, the recurrent architecture prevents parallel computation within a sequence and is highly time-consuming. To overcome this limitation, we propose a gated convolutional neural network (GCNN) for sentence matching tasks. In this model, the stacked convolutions encode hierarchical contextaware representations of a sentence, where the gating mechanism optionally controls and stores the convolutional contextual information. Furthermore, the attention mechanism is utilized to obtain interactive matching information between sentences. We evaluate our model on PI and NLI tasks, and the experiments demonstrate the advantages of the proposed approach in terms of both speed and accuracy performance.