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.

翻译：我们研究了自动机器翻译在在线视频教育领域的能力，通过使用最先进的翻译模型自动翻译可汗学院视频，并应用文本到语音合成及音视频同步技术，构建了目标语言中具有吸引力的视频。我们还基于往返翻译分析并建立了两种可靠的翻译置信度评估器，以有效管理翻译质量并减少人工翻译工作量。最后，我们开发了一个可部署的系统，用于向最终用户提供翻译后的视频，并收集用户修正以实现迭代改进。