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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 ...
Neural machine translation ... Koehn, Philipp (2020). Neural Machine Translation. ... Stahlberg, Felix (2020). Neural Machine Translation: A Review and Survey.
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 These methods have since been superseded by neural machine translation [1] and large language models ...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.
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
Google Neural Machine Translation (GNMT) was a neural machine translation (NMT) system developed by Google and introduced in November 2016 that used an artificial neural network to increase fluency and accuracy in Google Translate.
The term neural machine translation was coined by Bahdanau et al [18] and Sutskever et al [19] who also published the first research regarding this topic in 2014. Neural networks only needed a fraction of the memory needed by statistical models and whole sentences could be modeled in an integrated manner. The first large scale NMT was launched ...
In his paper "Europarl: A Parallel Corpus for Statistical Machine Translation", [1] Koehn sums up in how far the Europarl corpus is useful for research in SMT.He uses the corpus to develop SMT systems translating each language into each of the other ten languages of the corpus making it 110 systems.