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The past decade witnessed neural machine translation (NMT) methods replace statistical machine translation. 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 ...
The Georgetown–IBM experiment was an influential demonstration of machine translation, which was performed on January 7, 1954. Developed jointly by the Georgetown University and IBM, the experiment involved completely automatic translation of more than sixty Russian sentences into English. [1] [2]
On Linguistic Aspects of Translation is an essay written by Russian-American linguist Roman Jakobson in 1959. [1] It was published in On Translation, a compendium of seventeen papers edited by Reuben Arthur Brower. On Translation discusses various aspects of translation and was published in Cambridge, Massachusetts.
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.
Paper 2: Essay (25 marks weighing 25% of the course, 1 hour and 30 minutes for SL, 2 hours for HL) - Candidates write a comparative essay based on one of the three essay questions given for the literary genre studied in part 3 of the course.
A past paper is an examination paper from a previous year or previous years, usually used either for exam practice or for tests such as University of Oxford, [1] [2] University of Cambridge [3] College Collections.
Machine Translation Archive Archived April 1, 2019, at the Wayback Machine by John Hutchins. An electronic repository (and bibliography) of articles, books and papers in the field of machine translation and computer-based translation technology
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another. Quality is considered to be the correspondence between a machine's output and that of a human: "the closer a machine translation is to a professional human translation, the better it is" – this is the central idea behind BLEU.