  Sign language, essential for the deaf and hard-of-hearing, presents unique
challenges in translation and production due to its multimodal nature and the
inherent ambiguity in mapping sign language motion to spoken language words.
Previous methods often rely on gloss annotations, requiring time-intensive
labor and specialized expertise in sign language. Gloss-free methods have
emerged to address these limitations, but they often depend on external sign
language data or dictionaries, failing to completely eliminate the need for
gloss annotations. There is a clear demand for a comprehensive approach that
can supplant gloss annotations and be utilized for both Sign Language
Translation (SLT) and Sign Language Production (SLP). We introduce Universal
Gloss-level Representation (UniGloR), a unified and self-supervised solution
for both SLT and SLP, trained on multiple datasets including PHOENIX14T,
How2Sign, and NIASL2021. Our results demonstrate UniGloR's effectiveness in the
translation and production tasks. We further report an encouraging result for
the Sign Language Recognition (SLR) on previously unseen data. Our study
suggests that self-supervised learning can be made in a unified manner, paving
the way for innovative and practical applications in future research.
