  Sign language translation from video to spoken text presents unique
challenges owing to the distinct grammar, expression nuances, and high
variation of visual appearance across different speakers and contexts. The
intermediate gloss annotations of videos aim to guide the translation process.
In our work, we focus on {\em Gloss2Text} translation stage and propose several
advances by leveraging pre-trained large language models (LLMs), data
augmentation, and novel label-smoothing loss function exploiting gloss
translation ambiguities improving significantly the performance of
state-of-the-art approaches. Through extensive experiments and ablation studies
on the PHOENIX Weather 2014T dataset, our approach surpasses state-of-the-art
performance in {\em Gloss2Text} translation, indicating its efficacy in
addressing sign language translation and suggesting promising avenues for
future research and development.
