  Sign language translation (SLT) systems, which are often decomposed into
video-to-gloss (V2G) recognition and gloss-to-text (G2T) translation through
the pivot gloss, heavily relies on the availability of large-scale parallel G2T
pairs. However, the manual annotation of pivot gloss, which is a sequence of
transcribed written-language words in the order in which they are signed,
further exacerbates the scarcity of data for SLT. To address this issue, this
paper proposes a simple and efficient rule transformation method to transcribe
the large-scale target monolingual data into its pseudo glosses automatically
for enhancing the SLT translation. Empirical results show that the proposed
approach can significantly improve the performance of SLT, especially achieving
state-of-the-art results on two SLT benchmark datasets PHEONIX-WEATHER 2014T
and ASLG-PC12. Our code has been released at:
https://github.com/pengr/Mono\_SLT.
