  Sign Language Translation has attained considerable success recently, raising
hopes for improved communication with the Deaf. A pre-processing step called
tokenization improves the success of translations. Tokens can be learned from
sign videos if supervised data is available. However, data annotation at the
gloss level is costly, and annotated data is scarce. The paper utilizes
Adversarial, Multitask, Transfer Learning to search for semi-supervised
tokenization approaches without burden of additional labeling. It provides
extensive experiments to compare all the methods in different settings to
conduct a deeper analysis. In the case of no additional target annotation
besides sentences, the proposed methodology attains 13.25 BLUE-4 and 36.28
ROUGE scores which improves the current state-of-the-art by 4 points in BLUE-4
and 5 points in ROUGE.

Computer Vision and Pattern Recognition