Bilingual Corpus Mining and Multistage Fine-Tuning for Improving Machine
  Translation of Lecture Transcripts
Haiyue Song Raj Dabre Chenhui Chu Atsushi Fujita Sadao Kurohashi
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.