GNMT’s brilliant performances could be achieved by considering the broader context of a text to deduce the most fitting translation. This could cause funny (or embarrassing) translation errors. In fact, the former version translated the source language into English and then translated the English text into the target language.īut English, as much as all human languages, is ambiguous and context-related. The GNMT represented a massive improvement thanks to the possibility to manage zero-shot translations, in which a language is directly translated into another. ![]() The system went live two months later, replacing the old statistical approach used since 2007. Its architecture was first tested on more than a hundred languages supported by Google Translate. In September 2016, researchers announced the greatest leap in the history of Google Translate: the development of the Google Neural Machine Translation system (GNMT), based on a powerful artificial neural network with deep learning capabilities. Google Translate’s Neural network revolution ![]() Short answer? AI and neural networks applied to Linguistics. How did we improve from that kind of abomination to Google Translate’s most recent (and pretty impressive) performances? This phrase appeared on the website of the Italian Ministry of Education and Research some years ago when online translation tools, forgive my rudeness, literally sucked. “From sheep to Doggy Style traceability of milk chain in Tuscany.”Ĭonfused? Well, you are not the only one.
0 Comments
Leave a Reply. |