Applying Automated Machine Translation to Educational Video Courses
This work addresses the challenge of making online educational content accessible in multiple languages for learners, though it is incremental as it builds on existing translation and synthesis technologies.
The study tackled the problem of translating educational video courses by applying automated machine translation to Khan Academy videos, resulting in a deployable system that reduced human translation effort through confidence estimators and iterative user corrections.
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.