Explainable Semantic Communication for Text Tasks
Chuanhong Liu, Caili Guo, Yang Yang, Wanli Ni, Yanquan Zhou, Lei Li, Tony Q. S. Quek
Abstract
Task-oriented semantic communication has gained increasing attention due to its ability to reduce the amount of transmitted data without sacrificing task performance. Although some prior efforts have been dedicated to developing semantic communications, the semantics in these works remains to be unexplainable. Challenges related to explainable semantic representation and knowledge-based semantic compression have yet to be explored. In this article, we propose a triplet-based explainable semantic communication (TESC) scheme for representing text semantics efficiently. Specifically, we develop a semantic extraction method to convert text into triplets while using syntactic dependency analysis to enhance semantic completeness. Then, we design a semantic filtering method to further compress the duplicate and task-irrelevant triplets based on prior knowledge. The filtered triplets are encoded and transmitted to the receiver for completing intelligent tasks. Furthermore, we apply the proposed TESC scheme to two emblematic text tasks: 1) sentiment analysis and 2) question answering, in which the semantic codec is meticulously customized for each task. Experimental results demonstrate that 1) the TESC scheme outperforms benchmarks in terms of Top-1 accuracy and transmission efficiency and 2) the TESC scheme enjoys about 150% performance gain compared to the traditional communication method.