Applying Automated Machine Translation to Educational Video Courses
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 29 references that are in the map. First posted on arXiv 9 Jan 2023.
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References · 6
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2022AudioGen: Textually Guided Audio Generationcited by 292
- 2020Revisiting Round-Trip Translation for Quality Estimationcited by 24
- 2021Assessing Evaluation Metrics for Speech-to-Speech Translationcited by 10
- 2018Quality expectations of machine translationcited by 9
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