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   Unsupervised machine translation: A novel approach to provide fast,
   accurate translations for more languages
   Automatic language translation is important to Facebook as a way to
   machine translation (MT) systems require access to a considerable
   subset of languages for which a volume of translations is readily
   Training an MT model without access to any translation resources at
   training time (known as unsupervised translation) was the necessary
   reference translations. To give some idea of the level of advancement,
   available translation training resources is either nonexistent or so
   translations.
   This new method opens the door to faster, more accurate translations
   Word-by-word translation
   translations in the other language. For this, we used a method we
   and “furry” in English is similar to their corresponding translation in
   without access to any translation and essentially perform word-by-word
   translation.
   via a simple rotation (right). After the rotation, word translation is
   via a simple rotation (right). After the rotation, word translation is
   Word-by-word translation using a bilingual dictionary inferred in an
   unsupervised way is not a great translation — words may be missing, out
   initialization, we can now build an early version of a translation
   than word-by-word translation (thanks to the language model), and it
   Next, we treat these system translations (original sentence in Urdu,
   translation in English) as ground truth data to train an MT system in
   English sentences will be somewhat corrupt because of translation
   available), and it was dubbed back translation. This is the first time
   sentences from our back translation with the corrections provided by
   the Urdu language model to train a translation system from English to
   (original sentence in English, translation in Urdu) that can help
   En-Ur MT system. Next, the Ur-En MT system takes that Urdu translation
   as input and produces the English translation. The error between “cats
   initialization, language modeling, and back translation — as important
   than word-by-word translations but did not produce translations of the
   back-translation sentences. With back translation, this method
   translations had the correct words but were less fluent. Again, this
   German-to-English translation examples show the results of each method:
   German-to-English translation examples show the results of each machine
   translation method
   German-to-English translation examples show the results of each machine
   translation method
   translate in many more languages and improve translation quality for
   demonstrations (translations, in this case) that are currently
   esearch/unsupervised-machine-translation-a-novel-approach-to-provide-fa
   st-accurate-translations-for-more-languages/&width=450&layout=standard&
   Transitioning entirely to neural machine translation