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.
| Original language | English |
|---|---|
| Article number | 3418208 |
| Journal | ACM Transactions on Internet Technology |
| Volume | 21 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 15 Mar 2021 |
Bibliographical note
Funding Information:P. Ni and Y. Li are also with the University of Liverpool. 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). Authors’ addresses: P. Ni and Y. Li, The University of Auckland, Auckland, New Zealand; emails: {pni641, yuming.li}@ auckland.ac.nz; G. Li, Xi’an Jiaotong-Liverpool University, Suzhou, China; email: [email protected]; V. Chang, Teesside University, Middlesbrough, UK; email: [email protected]. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2021 Association for Computing Machinery. 1533-5399/2021/03-ART33 $15.00 https://doi.org/10.1145/3418208
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© 2021 Association for Computing Machinery.