  Sign language gloss translation aims to translate the sign glosses into
spoken language texts, which is challenging due to the scarcity of labeled
gloss-text parallel data. Back translation (BT), which generates
pseudo-parallel data by translating in-domain spoken language texts into sign
glosses, has been applied to alleviate the data scarcity problem. However, the
lack of large-scale high-quality domain spoken language text data limits the
effect of BT. In this paper, to overcome the limitation, we propose a Prompt
based domain text Generation (PGEN) approach to produce the large-scale
in-domain spoken language text data. Specifically, PGEN randomly concatenates
sentences from the original in-domain spoken language text data as prompts to
induce a pre-trained language model (i.e., GPT-2) to generate spoken language
texts in a similar style. Experimental results on three benchmarks of sign
language gloss translation in varied languages demonstrate that BT with spoken
language texts generated by PGEN significantly outperforms the compared
methods. In addition, as the scale of spoken language texts generated by PGEN
increases, the BT technique can achieve further improvements, demonstrating the
effectiveness of our approach. We release the code and data for facilitating
future research in this field.
