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Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an artificial neural network to increase fluency and accuracy in Google Translate.
Based on these RNN-based architectures, Baidu launched the "first large-scale NMT system" [23]: 144 in 2015, followed by Google Neural Machine Translation in 2016. [23]: 144 [24] From that year on, neural models also became the prevailing choice in the main machine translation conference Workshop on Statistical Machine Translation. [25]
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]
The Google Brain team contributed to the Google Translate project by employing a new deep learning system that combines artificial neural networks with vast databases of multilingual texts. [21] In September 2016, Google Neural Machine Translation (GNMT) was launched, an end-to-end learning framework, able to learn from a large number of ...
By the 2010s, the LSTM became the dominant technique for a variety of natural language processing tasks including speech recognition and machine translation, and was widely implemented in commercial technologies such as Google Neural Machine Translation, [24] have also been used in Google Voice for transcription [25] and search, [26] and Siri. [27]
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
In 2016, Google Translate was revamped to Google Neural Machine Translation, which replaced the previous model based on statistical machine translation. The new model was a seq2seq model where the encoder and the decoder were both 8 layers of bidirectional LSTM. [26]
Shannon's diagram of a general communications system, showing the process by which a message sent becomes the message received (possibly corrupted by noise). seq2seq is an approach to machine translation (or more generally, sequence transduction) with roots in information theory, where communication is understood as an encode-transmit-decode process, and machine translation can be studied as a ...