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A 2018 paper by the University of Bologna evaluated the Italian-to-German translation capabilities and found the preliminary results to be similar in quality to Google Translate. [ 42 ] In September 2021, Slator remarked that the language industry response was more measured than the press and noted that DeepL is still highly regarded by users.
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]
By 2020, the system had been replaced by another deep learning system based on a Transformer encoder and an RNN decoder. [ 10 ] 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 ]
In order to be competitive on the machine translation task, LLMs need to be much larger than other NMT systems. E.g., GPT-3 has 175 billion parameters, [40]: 5 while mBART has 680 million [34]: 727 and the original transformer-big has “only” 213 million. [31]: 9 This means that they are computationally more expensive to train and use.
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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]
On October 25, 2019, Google announced that they had started applying BERT models for English language search queries within the US. [26] On December 9, 2019, it was reported that BERT had been adopted by Google Search for over 70 languages. [27] [28] In October 2020, almost every single English-based query was processed by a BERT model. [29]
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
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