Home / News & Analysis / Google brings offline neural machine translations for 59 languages to its Translate app

Google brings offline neural machine translations for 59 languages to its Translate app

Currently, when the Google Translate apps for iOS and Android has access to the internet, its translations are far superior to those it produces when it’s offline. That’s because the offline translations are phrase-based, meaning they use an older machine translation technique than the machine learning-powered systems in the cloud that the app has access to when it’s online. But that’s changing today. Google is now rolling out offline Neural Machine Translation (NMT) support for 59 languages in the Translate apps.

Today, only a small number of users will see the updated offline translations, but it will roll out to all users within the next few weeks.

The list of supported languages consists of a wide range of languages. Because I don’t want to play favorites, here is the full list: Afrikaans, Albanian, Arabic, Belarusian, Bengali, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian, Creole, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Jannada, Korean, Lavtian, Lithuanian, Macedonian, Malay, Maltese, Marathi, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Vietnamese and Welsh.

In the past, running these deep learning models on a mobile device wasn’t really an option since mobile phones didn’t have the right hardware to efficiently run them. Now, thanks to both advances in hardware and software, that’s less of an issue and Google, Microsoft and others have also found ways to compress these models to a manageable size. In Google’s case, that’s about 30 to 40 megabytes per language.

It’s worth noting that Microsoft also announced a similar feature for its Translator app earlier this year. It uses a very similar technique but for the time being, it only supports about a dozen languages.

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