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A hybrid Siamese neural network for natural language inference in cyber-physical systems

  • Gangmin Li
    ,
  • Pin Ni
    ,
  • Yuming Li
    ,
  • Victor Chang
  • The University of Auckland
    ,
  • Teesside University
Research Output: Contribution to journal Article Peer-review

Open access

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Article number

3418208

Pages from-to (Number of pages)

Pages 1-25

Journal (Volume, Issue Number)

ACM Transactions on Internet Technology (Volume 21, Issue 2)

Publication milestones

  • Accepted/In press - 01/07/2020
  • Published - 15/03/2021

Publication status

Published - 15/03/2021

ISSN

1533-5399

External Publication IDs

  • handle.net: 10547/625924
  • Scopus: 85114284006

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