Journal of Applied Science and Engineering

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Hao Zhang1, HuaXiong Zhang This email address is being protected from spambots. You need JavaScript enabled to view it.1, XingYu Lu2, and Qiang Gao3

1Zhejiang Sci-Tech University, Hangzhou, China
2Northeastern University, Boston, MA, USA
3Communication University of Zhejiang, Hangzhou, China


Received: January 28, 2021
Accepted: July 10, 2021
Publication Date: August 16, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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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 dataset, 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 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.

Keywords: Chinese semantic textual similarity; convolutional neural network; attention mechanism; ABOEN


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