We present an approach based on multilingual sentence embeddings to automatically extract parallel sentences from the content of WikiPedia articles in 85 languages, including several dialects or low-resource languages. We do not limit the the extraction process to alignments with English, but systematically consider all possible language pairs. In total, we are able to extract 135M parallel sentences for 1620 different language pairs, out of which only 34M are aligned with English. This corpus of parallel sentences is freely available at https://github.com/facebookresearch/LASER/tasks/WikiMatrix. To get an indication on the quality of the extracted bitexts, we train neural MT baseline systems on the mined data only for 1886 languages pairs, and evaluate them on the TED corpus, achieving strong BLEU scores for many language pairs. The WikiMatrix bitexts seem to be particularly interesting to train MT systems between distant languages without the need to pivot through English.

使用多语句子嵌入的方法自动从85种语言的维基百科文章中提取平行句子，共提取了1.35亿个平行句子，并且在1886种语言对上训练神经机器翻译基线系统，取得了较高的BLEU分数。WikiMatrix bitexts可以对离散的语言进行机器翻译训练，而不需要通过英语来进行桥接。

WikiMatrix: 从维基百科挖掘出 1620 种语言对中的 1.35 亿个平行句子