  In this paper, we tackle the problem of sign language translation (SLT)
without gloss annotations. Although intermediate representation like gloss has
been proven effective, gloss annotations are hard to acquire, especially in
large quantities. This limits the domain coverage of translation datasets, thus
handicapping real-world applications. To mitigate this problem, we design the
Gloss-Free End-to-end sign language translation framework (GloFE). Our method
improves the performance of SLT in the gloss-free setting by exploiting the
shared underlying semantics of signs and the corresponding spoken translation.
Common concepts are extracted from the text and used as a weak form of
intermediate representation. The global embedding of these concepts is used as
a query for cross-attention to find the corresponding information within the
learned visual features. In a contrastive manner, we encourage the similarity
of query results between samples containing such concepts and decrease those
that do not. We obtained state-of-the-art results on large-scale datasets,
including OpenASL and How2Sign. The code and model will be available at
https://github.com/HenryLittle/GloFE.
