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Logos Corporation was founded by Bernard (Bud) Scott in 1970, who worked on its Logos Machine Translation System until the company's dissolution in 2000. The project began as an English-Vietnamese translation system, which became operational in 1972 (during the American- Vietnam War ), and later was developed as a multi-target translation ...
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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]
PILLS (Patient Information Language Localisation System) is a one-year effort by the European Commission to produce a prototype tool which will support the creation of various kinds of medical documentation simultaneously in multiple languages, by storing the information in a database and allowing a variety of forms and languages of output.
In a rule-based machine translation system the original text is first analysed morphologically and syntactically in order to obtain a syntactic representation. This representation can then be refined to a more abstract level putting emphasis on the parts relevant for translation and ignoring other types of information.
Over a thousand fans welcomed former boxing champion Mike Tyson to Neptune during Tyson's promotional tour of his own brand of cannabis products.
Speech translation is the process by which conversational spoken phrases are instantly translated and spoken aloud in a second language. This differs from phrase translation, which is where the system only translates a fixed and finite set of phrases that have been manually entered into the system.
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.