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Google Translate is a multilingual neural machine translation service developed by Google to translate text, documents and websites from one language into another. It offers a website interface, a mobile app for Android and iOS, as well as an API that helps developers build browser extensions and software applications. [3]
Apertium wiki (list of language pairs and licence information) Xerox Easy Translator Service (list of language pairs) Bing Translator Language List; Haitian Creole support in Bing/Microsoft Translator; Microsoft Research: Syntactically Informed Phrasal SMT; List of supported languages in Google Translate
GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2] GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. [2]
The notable rise of social networking on the web in recent years has created yet another niche for the application of machine translation software – in utilities such as Facebook, or instant messaging clients such as Skype, Google Talk, MSN Messenger, etc. – allowing users speaking different languages to communicate with each other.
Generative language models are not trained on the translation task, let alone on a parallel dataset. Instead, they are trained on a language modeling objective, such as predicting the next word in a sequence drawn from a large dataset of text. This dataset can contain documents in many languages, but is in practice dominated by English text. [36]
The accuracy of Google Translate continues to improve, and in many cases approaches the accuracy of human translation; Use of non-English sources can help counter systemic bias on Wikipedia, which skews to Anglocentric and Eurocentric perspectives; Cons. Accuracy may not be sufficient for all uses, and human translation is still more accurate
Language models are typically approximated by smoothed n-gram models, and similar approaches have been applied to translation models, but this introduces additional complexity due to different sentence lengths and word orders in the languages. Statistical translation models were initially word based (Models 1-5 from IBM Hidden Markov model from ...
Users could then review and improve the automatic translation by clicking on the sentence and fixing a translation, or using Google's translation tools to help them translate by clicking the "Show toolkit" button. Users could view translations previously entered by other users in the "Translation search results" tab or use the "Dictionary" tab ...
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