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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.
Philipp Koehn (born 1 August 1971 in Erlangen, West Germany) is a computer scientist and researcher in the field of machine translation. [1] [2] His primary research interest is statistical machine translation and he is one of the inventors of a method called phrase based machine translation. This is a sub-field of statistical 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 These methods have since been superseded by neural machine translation [1] and large language models ...
Statistical machine translation was re-introduced in the late 1980s and early 1990s by researchers at IBM's Thomas J. Watson Research Center. [3] [4] [5] Before the introduction of neural machine translation, it was by far the most widely studied machine translation method.
In November 2016, Google Neural Machine Translation system (GNMT) was introduced. Since then, Google Translate began using neural machine translation (NMT) in preference to its previous statistical methods (SMT) [ 1 ] [ 16 ] [ 17 ] [ 18 ] which had been used since October 2007, with its proprietary, in-house SMT technology.
Neural machine translation models available through the Watson Language Translator API for developers. [4] [5] Microsoft Translator: Cross-platform (web application) SaaS: No fee required: Final: No: 100+ Statistical and neural machine translation: Moses: Cross-platform: LGPL: No fee required: 4.0 [6] Yes
Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh. [2] Moses then allows new source-language text to be decoded using these models to produce automatic translations in the target language.
Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively.