  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 paper, 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 propsed TESC scheme to two emblematic text tasks: sentiment analysis
and question answering, in which the semantic codec is meticulously customized
for each task. Experimental results demonstrate that 1) TESC scheme outperforms
benchmarks in terms of Top-1 accuracy and transmission efficiency, and 2) TESC
scheme enjoys about 150% performance gain compared to the traditional
communication method.
