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The ASCII text-encoding standard uses 7 bits to encode characters. With this it is possible to encode 128 (i.e. 2 7) unique values (0–127) to represent the alphabetic, numeric, and punctuation characters commonly used in English, plus a selection of Control characters which do not represent printable characters.
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 following table compares the number of languages which the following machine translation programs can translate between. (Moses and Moses for Mere Mortals allow you to train translation models for any language pair, though collections of translated texts (parallel corpus) need to be provided by the user.
Timerio is a constructed language based on numbers. It was presented to public in 1921 by the Berlin architect Tiemer as a pure literary language and should be used for automated translations. [1] The idea was, that every concept is assigned by a number. The language shows similarities to the Dewey Decimal Classification by Melvil Dewey.
Google Translate previously first translated the source language into English and then translated the English into the target language rather than translating directly from one language to another. [11] A July 2019 study in Annals of Internal Medicine found that "Google Translate is a viable, accurate tool for translating non–English-language ...
This table lists all two-letter codes (set 1), one per language for ISO 639 macrolanguage, and some of the three-letter codes of the other sets, formerly parts 2 and 3. Entries in the Scope column distinguish: Individual language; Collections of related languages; Macrolanguages; The Type column distinguishes: Ancient languages (extinct since ...
Dynamic binary translation (DBT) looks at a short sequence of code—typically on the order of a single basic block—then translates it and caches the resulting sequence. Code is only translated as it is discovered and when possible, and branch instructions are made to point to already translated and saved code ( memoization ).
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]