Lecture transcript translation helps learners understand online courses, however, building a high-quality lecture machine translation system lacks publicly available parallel corpora. To address this, we examine a framework for parallel corpus mining, which provides a quick and effective way to mine a parallel corpus from publicly available lectures on Coursera. To create the parallel corpora, we propose a dynamic programming based sentence alignment algorithm which leverages the cosine similarity of machine-translated sentences. The sentence alignment F1 score reaches 96%, which is higher than using the BERTScore, LASER, or sentBERT methods. For both English--Japanese and English--Chinese lecture translations, we extracted parallel corpora of approximately 50,000 lines and created development and test sets through manual filtering for benchmarking translation performance. Through machine translation experiments, we show that the mined corpora enhance the quality of lecture transcript translation when used in conjunction with out-of-domain parallel corpora via multistage fine-tuning. Furthermore, this study also suggests guidelines for gathering and cleaning corpora, mining parallel sentences, cleaning noise in the mined data, and creating high-quality evaluation splits. For the sake of reproducibility, we have released the corpora as well as the code to create them. The dataset is available at https://github.com/shyyhs/CourseraParallelCorpusMining.

翻译：讲座字幕翻译有助于学习者理解在线课程，然而构建高质量讲座机器翻译系统缺乏公开可用的平行语料库。为此，我们研究了一种平行语料库挖掘框架，该框架提供了一种从Coursera公开讲座中快速有效地挖掘平行语料库的方法。为创建平行语料库，我们提出了一种基于动态规划的句子对齐算法，该算法利用机器翻译句子的余弦相似度。句子对齐F1分数达到96%，高于使用BERTScore、LASER或sentBERT方法的结果。针对英语-日语和英语-中文讲座翻译，我们提取了约5万行平行语料库，并通过人工筛选创建了用于翻译性能基准测试的开发集和测试集。通过机器翻译实验，我们证明在结合域外平行语料库进行多阶段微调时，所挖掘的语料库能提升讲座字幕翻译质量。此外，本研究还提出了收集清理语料库、挖掘平行句子、清理挖掘数据噪声以及创建高质量评估划分的指导方针。为保障可复现性，我们已公开发布该语料库及其创建代码。数据集可在https://github.com/shyyhs/CourseraParallelCorpusMining获取。