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

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

Ni, Pin; Li, Yuming; Li, Gangmin; Chang, Victor; (2021) A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems. ACM Transactions on Internet Technology , 21 (2) , Article 33. 10.1145/3418208. Green open access

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Abstract

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.

Type:	Article
Title:	A Hybrid Siamese Neural Network for Natural Language Inference in Cyber-Physical Systems
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1145/3418208
Publisher version:	https://doi.org/10.1145/3418208
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Siamese neural networks, Natural language inference, Cyber-physical systems
UCL classification:	UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng UCL > Provost and Vice Provost Offices > UCL BEAMS UCL
URI:	https://discovery.ucl.ac.uk/id/eprint/10157988