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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.
Both rule-based and statistical models developed by IBM Research. 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 ...
NiuTrans.SMT is an open-source statistical machine translation system jointly developed by the Natural Language Processing Laboratory of Northeastern University and Shenyang Yayi Network Technology Co., Ltd. NiuTrans.NMT is a lightweight and efficient Transformer-based neural machine translation system.
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.
A rendition of the Vauquois triangle, illustrating the various approaches to the design of machine translation systems.. The direct, transfer-based machine translation and interlingual machine translation methods of machine translation all belong to RBMT but differ in the depth of analysis of the source language and the extent to which they attempt to reach a language-independent ...
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]
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.
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.