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Sign language translation (SLT) systems, which are often decomposed intovideo-to-gloss (V2G) recognition and gloss-to-text (G2T) translation throughthe pivot gloss, heavily relies on the availability of large-scale parallel G2Tpairs. However, the manual annotation of pivot gloss, which is a sequence oftranscribed written-language words in the order in which they are signed,further exacerbates the scarcity of data for SLT. To address this issue, thispaper proposes a simple and efficient rule transformation method to transcribethe large-scale target monolingual data into its pseudo glosses automaticallyfor enhancing the SLT translation. Empirical results show that the proposedapproach can significantly improve the performance of SLT, especially achievingstate-of-the-art results on two SLT benchmark datasets PHEONIX-WEATHER 2014Tand ASLG-PC12. Our code has been released at:https://github.com/pengr/Mono\_SLT.

Better Sign Language Translation with Monolingual Data