Neural Machine Translation (
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Neural Machine Translation (
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model. It is the dominant approach today and can produce translations that rival human translations when translating between high-resource languages under specific conditions. However, there still remain challenges, especially with languages where less high-quality data is available, and with Domain adaptation#Domain shift, domain shift between the data a system was trained on and the texts it is supposed to translate. NMT systems also tend to produce fairly literal translations. Overview In the translation task, a sentence \mathbf = x_ (consisting of I tokens x_i) in the source language is to be translated into a sentence \mathbf = x_ (consisting of J tokens x_j) in the target language. The source and target tokens (which in the simple event are used for each other ...
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Machine Translation
Machine translation is use of computational techniques to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Early approaches were mostly rule-based or statistical. These methods have since been superseded by neural machine translation and large language models. History Origins The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation. The idea of machine translation later appeared in the 17th century. In 1629, René Descartes proposed a universal language, with equivalent ideas in different tongues sharing one symbol. The idea of using digital computers for translation of natural languages was proposed as early as 1947 by England's A. D. Booth and Warr ...
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