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FeMoco (FeMo cofactor) is the primary cofactor of nitrogenase. Nitrogenase is the enzyme that catalyzes the conversion of atmospheric nitrogen molecules N 2 into ammonia (NH 3) through the process known as nitrogen fixation. Because it contains iron and molybdenum, the cofactor is called FeMoco. Its stoichiometry is Fe 7 MoS 9 C.
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 ]
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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 ...
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.
Google Translator Toolkit was [1] an online computer-assisted translation tool (CAT)—a web application designed to permit translators to edit the translations that Google Translate automatically generated using its own and/or user-uploaded files of appropriate glossaries and translation memory.
A DMT system is designed for a specific source and target language pair and the translation unit of which is usually a word. Translation is then performed on representations of the source sentence structure and meaning respectively through syntactic and semantic transfer approaches. A transfer-based machine translation system involves three ...
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]
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