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Machine translation is highly sensitive to the size and quality of thetraining data, which has led to an increasing interest in collecting andfiltering large parallel corpora. In this paper, we propose a new method forthis task based on multilingual sentence embeddings. In contrast to previousapproaches, which rely on nearest neighbor retrieval with a hard threshold overcosine similarity, our proposed method accounts for the scale inconsistenciesof this measure, considering the margin between a given sentence pair and itsclosest candidates instead. Our experiments show large improvements overexisting methods. We outperform the best published results on the BUCC miningtask and the UN reconstruction task by more than 10 F1 and 30 precision points,respectively. Filtering the English-German ParaCrawl corpus with our approach,we obtain 31.2 BLEU points on newstest2014, an improvement of more than onepoint over the best official filtered version.

Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings