  We studied the capability of automated machine translation in the online
video education space by automatically translating Khan Academy videos with
state-of-the-art translation models and applying text-to-speech synthesis and
audio/video synchronization to build engaging videos in target languages. We
also analyzed and established two reliable translation confidence estimators
based on round-trip translations in order to efficiently manage translation
quality and reduce human translation effort. Finally, we developed a deployable
system to deliver translated videos to end users and collect user corrections
for iterative improvement.
