Facebook Develops New AI Technique For Language Translation

Researchers at Facebook have developed a quicker and more accurate way of translating languages using artificial intelligence. They claim that the new approach can also help computers to translate more languages including low-resource languages like Urdu and Burmese.

Existing machine translation systems can achieve near human-level performance on some languages but they require access to parallel corpus — vast quantities of the same sentences in different languages — in order to learn.

The team from the Facebook AI Research (FAIR) division were able to train a machine translation (MT) system by feeding it large pieces of different text in different languages from publicly available websites like Wikipedia. The key thing to note is that these pieces of text were independent of one another. When you have different pieces of text in different languages they're referred to as monolingual corpora.

Antoine Bordes, a research scientist and the head of FAIR's Paris research lab, said: "Building a parallel corpus is complicated because you need to find people fluent in two languages to create it. For instance, if you wanted to build a parallel corpus of Portuguese/Nepali, you would need to find people fluent in these two languages, which would be very difficult. On the other side, building monolingual corpora Portuguese/Nepali is very easy: you just need to download webpages from Portuguese and from Nepali websites, it doesn't matter if they are not parallel sentences or if they talk about different things."

Most language translation computer systems use both monolingual corpora and parallel corpus to learn.

Bordes added: "The novelty in our approach is that we can train MT systems from monolingual corpora only, we don't need any parallel corpus. Potentially, given a book written in an alien language, we could use our model to translate it into English."

The breakthrough, which will be presented at Empirical Methods in Natural Language Processing or EMNLP, could prove to be important for Facebook, as the social media giant uses automatic language translation to help its users around the world to read posts in their preferred language.

"Our new approach provides a dramatic improvement over previous state-of-the-art unsupervised approaches — and is equivalent to supervised approaches trained with nearly 100,000 reference translations," wrote FAIR research scientist Marc'Aurelio Ranzato in a blog post. "To give some idea of the level of advancement, an improvement of 1 BLEU point (a common metric for judging the accuracy of MT) is considered a remarkable achievement in this field — our methods showed an improvement of more than 10 BLEU points.




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