  Existing work on sign language translation - that is, translation from sign
language videos into sentences in a written language - has focused mainly on
(1) data collected in a controlled environment or (2) data in a specific
domain, which limits the applicability to real-world settings. In this paper,
we introduce OpenASL, a large-scale American Sign Language (ASL) - English
dataset collected from online video sites (e.g., YouTube). OpenASL contains 288
hours of ASL videos in multiple domains from over 200 signers and is the
largest publicly available ASL translation dataset to date. To tackle the
challenges of sign language translation in realistic settings and without
glosses, we propose a set of techniques including sign search as a pretext task
for pre-training and fusion of mouthing and handshape features. The proposed
techniques produce consistent and large improvements in translation quality,
over baseline models based on prior work. Our data and code are publicly
available at https://github.com/chevalierNoir/OpenASL
