  Recently, semantic communication has been brought to the forefront because of
its great success in deep learning (DL), especially Transformer. Even if
semantic communication has been successfully applied in the sentence
transmission to reduce semantic errors, existing architecture is usually fixed
in the codeword length and is inefficient and inflexible for the varying
sentence length. In this paper, we exploit hybrid automatic repeat request
(HARQ) to reduce semantic transmission error further. We first combine semantic
coding (SC) with Reed Solomon (RS) channel coding and HARQ, called SC-RS-HARQ,
which exploits the superiority of the SC and the reliability of the
conventional methods successfully. Although the SC-RS-HARQ is easily applied in
the existing HARQ systems, we also develop an end-to-end architecture, called
SCHARQ, to pursue the performance further. Numerical results demonstrate that
SCHARQ significantly reduces the required number of bits for sentence semantic
transmission and sentence error rate. Finally, we attempt to replace error
detection from cyclic redundancy check to a similarity detection network called
Sim32 to allow the receiver to reserve the wrong sentences with similar
semantic information and to save transmission resources.
