Sentence Semantic Matching with Hierarchical CNN Based on Dimension-augmented Representation

Abstract

As a fundamental task in natural language processing, sentence semantic matching (SSM) is critical yet challenging due to difficulties in learning expressive sentence representation while capturing complex interactions between sentences. Recent work has shown the great potential of deep neural models in improving the performance of SSM task. However, existing work usually employs recurrent neural networks (RNNs) or 1D (one-dimensional) convolutional neural networks (CNNs) to learn sentence representation, leading to limited performance improvement. Benefiting from the multi-dimensional structure, 2D convolutional neural networks are expected to be more powerful to learn expressive sentence representation by capturing the implicit inter-sentence interactions and thus can further improve the performance of SSM. To this end, in this paper, we propose a novel sentence semantic matching model named Hierarchical CNN based on Dimension-augmented Representation (HiDR). In HiDR, first, bidirectional long short-term memory networks (LSTMs) are utilized to generate dimension-augmented representation for each of the input sentences; then, a hierarchical 2D CNN is devised to learn sentence representation while capturing the inter-sentence interactions, followed by a prediction layer based on sigmoid function to output the matching degree between sentences. To evaluate the performance of our proposed model, we conducted extensive experiments on two public real-world data sets. The empirical results show that HiDR has achieved remarkable results, which demonstrates either better or comparable performance w.r.t. BERT-based models.