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GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. [2] With the large end-to-end framework, the system learns over time to create better, more natural translations. [1] GNMT attempts to translate whole sentences at a time, rather than just piece by piece. [1]
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]
Google Search (also known simply as Google or Google.com) is a search engine operated by Google. It allows users to search for information on the Web by entering keywords or phrases. Google Search uses algorithms to analyze and rank websites based on their relevance to the search query. It is the most popular search engine worldwide.
Bidirectional encoder representations from transformers (BERT) is a language model introduced in October 2018 by researchers at Google. [1] [2] It learns to represent text as a sequence of vectors using self-supervised learning.
Google Dictionary is an online dictionary service of Google that can be accessed with the "define" operator and other similar phrases [note 1] in Google Search. [2] It is also available in Google Translate and as a Google Chrome extension. The dictionary content is licensed from Oxford University Press's Oxford Languages. [3]
Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words.
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