首页|On Harnessing Semantic Communication With Natural Language Processing
On Harnessing Semantic Communication With Natural Language Processing
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NETL
NSTL
IEEE
Through experimental endeavors, we explore the intersection of semantic communication (SemCom) and natural language processing (NLP) to address gaps in SemCom models, focusing on reducing ambiguity and enhancing 6G communication. Our approach involves two phases: 1) Phase 1: Designing an NLP-based system for fair classification, leveraging techniques, such as unsupervised style transfer and zero-shot learning to align human intuition with semantic specifications. 2) Phase 2: Developing modules to minimize and evaluate SemCom performance under channel impairments, integrating language models like DistilBERT and RoBERTa. Results are evaluated using area under the curve receiver operating characteristic (AUC-ROC) metrics across diverse classifiers. Implementation details are publicly available on GitHub. We provide invaluable insights toward learning to harness SemCom with NLP.
RobustnessNatural language processingToxicologyEncodingAdaptation models6G mobile communicationSemantic communicationProtocolsInternet of ThingsWireless communication
Shiva Raj Pokhrel、Te’ Claire
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Internet of Things and Systems Engineering Laboratory, School of Information Technology, Deakin University, Geelong, VIC, Australia
NETL
NSTL
IEEE
