# Applying Automated Machine Translation to Educational Video Courses

arXiv: https://arxiv.org/abs/2301.03141 · HTML: https://mlsanity.com/paper/2301.03141/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 6 of 6)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [AudioGen: Textually Guided Audio Generation](https://mlsanity.com/paper/2209.15352/) (2022)
- [Revisiting Round-Trip Translation for Quality Estimation](https://mlsanity.com/paper/2004.13937/) (2020)
- [Assessing Evaluation Metrics for Speech-to-Speech Translation](https://mlsanity.com/paper/2110.13877/) (2021)
- [Quality expectations of machine translation](https://mlsanity.com/paper/1803.08409/) (2018)

## 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.