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
this https URL 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。