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Lectures translation is a case of spoken language translation and there is alack of publicly available parallel corpora for this purpose. To address this,we examine a language independent framework for parallel corpus mining which isa quick and effective way to mine a parallel corpus from publicly availablelectures at Coursera. Our approach determines sentence alignments, relying onmachine translation and cosine similarity over continuous-space sentencerepresentations. We also show how to use the resulting corpora in a multistagefine-tuning based domain adaptation for high-quality lectures translation. ForJapanese--English lectures translation, we extracted parallel data ofapproximately 40,000 lines and created development and test sets through manualfiltering for benchmarking translation performance. We demonstrate that themined corpus greatly enhances the quality of translation when used inconjunction with out-of-domain parallel corpora via multistage training. Thispaper also suggests some guidelines to gather and clean corpora, mine parallelsentences, address noise in the mined data, and create high-quality evaluationsplits. For the sake of reproducibility, we will release our code for paralleldata creation.

Coursera Corpus Mining and Multistage Fine-Tuning for Improving Lectures Translation