A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems


Cyber-Physical Systems (CPS), as a multi-dimensional complex system that connects the physical world and the cyber world, has a strong demand for processing large amounts of heterogeneous data. These tasks also include Natural Language Inference (NLI) tasks based on text from different sources. However, the current research on natural language processing in CPS does not involve exploration in this field. Therefore, this study proposes a Siamese Network structure that combines Stacked Residual Long Short-Term Memory (bidirectional) with the Attention mechanism and Capsule Network for the NLI module in CPS, which is used to infer the relationship between text/language data from different sources. This model is mainly used to implement NLI tasks and conduct a detailed evaluation in three main NLI benchmarks as the basic semantic understanding module in CPS. Comparative experiments prove that the proposed method achieves competitive performance, has a certain generalization ability, and can balance the performance and the number of trained parameters.

Publication DOI: https://doi.org/10.1145/3418208
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 2021 ACM Funding Information: This research was partly funded by VC Research (VCR 0000059). At the same time, this study is also partially supported by the AI University Research Centre (AI-URC) through the XJTLU Key Program Special Fund (KSF-P-02) and KSF-A-17. And this work has received support from the Suzhou Bureau of Sci. and Tech. and the Key Industrial Tech. Inno. program (No. SYG201840).
Uncontrolled Keywords: Cyber-physical systems,Natural language inference,Siamese neural networks,Computer Networks and Communications
Publication ISSN: 1557-6051
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://dl.acm. ... 10.1145/3418208 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-06-01
Authors: Ni, Pin
Li, Yuming
Li, Gangmin
Chang, Victor (ORCID Profile 0000-0002-8012-5852)



Version: Accepted Version

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