Gloss Attention for Gloss-free Sign Language Translation
Aoxiong Yin Tianyun Zhong Li Tang Weike Jin Tao Jin Zhou Zhao
Most sign language translation (SLT) methods to date require the use of gloss
annotations to provide additional supervision information, however, the
acquisition of gloss is not easy. To solve this problem, we first perform an
analysis of existing models to confirm how gloss annotations make SLT easier.
We find that it can provide two aspects of information for the model, 1) it can
help the model implicitly learn the location of semantic boundaries in
continuous sign language videos, 2) it can help the model understand the sign
language video globally. We then propose \emph{gloss attention}, which enables
the model to keep its attention within video segments that have the same
semantics locally, just as gloss helps existing models do. Furthermore, we
transfer the knowledge of sentence-to-sentence similarity from the natural
language model to our gloss attention SLT network (GASLT) to help it understand
sign language videos at the sentence level. Experimental results on multiple
large-scale sign language datasets show that our proposed GASLT model
significantly outperforms existing methods. Our code is provided in
\url{https://github.com/YinAoXiong/GASLT}.