  Machine learning for sign languages is bottlenecked by data. In this paper,
we present YouTube-ASL, a large-scale, open-domain corpus of American Sign
Language (ASL) videos and accompanying English captions drawn from YouTube.
With ~1000 hours of videos and >2500 unique signers, YouTube-ASL is ~3x as
large and has ~10x as many unique signers as the largest prior ASL dataset. We
train baseline models for ASL to English translation on YouTube-ASL and
evaluate them on How2Sign, where we achieve a new finetuned state of the art of
12.39 BLEU and, for the first time, report zero-shot results.
