Attention-Based Overall Enhance Network for Chinese Semantic Textual Similarity Measure

Semantic text similarity(STS) measure plays an important role in the practical application of natural language processing. However, due to the complexity of Chinese semantic comprehension and the lack of currently available Chinese text similarity datasets, present research on Chinese semantic text similarity still exists many limitations. In this paper, we construct a new private self-built Chinese semantic similarity (NCSS) dataset and propose a new method called Attention-based Overall Enhance Network (ABOEN) for measuring semantic textual similarity. This model takes advantage of a convolutional neural network upon soft attention layers to capture more fine-grained interactive features between two sentences. Besides, inspired by the channel attention mechanism in image classification, we adopt a channel attention mechanism to enhance the critical overall interactive features between two sentences. The experimental results show that compared with other baseline models, the accuracy based on our model on the NCSS and LCQMC datasets has increased by 1.38% and 1.49%, respectively, which proves the effectiveness of our proposed model