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

Author:

Ni Pin1,Li Yuming1,Li Gangmin2,Chang Victor3

Affiliation:

1. The University of Auckland

2. Xi’an Jiaotong-Liverpool University

3. Teesside University

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.

Funder

VC Research

AI University Research Centre (AI-URC) through the XJTLU Key Program Special Fund

Suzhou Bureau of Sci. and Tech. and the Key Industrial Tech. Inno. program

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Reference107 articles.

1. A question-entailment approach to question answering;Abacha Asma Ben;BMC Bioinformatics,2019

2. A survey of paraphrasing and textual entailment methods;Androutsopoulos Ion;Journal of Artificial Intelligence Research,2010

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