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===Statistical=== {{main|Statistical machine translation}} Statistical machine translation tried to generate translations using [[statistical methods]] based on bilingual text corpora, such as the [[Hansard#Translation|Canadian Hansard]] corpus, the English-French record of the Canadian parliament and [[Europarl corpus|EUROPARL]], the record of the [[European Parliament]]. Where such corpora were available, good results were achieved translating similar texts, but such corpora were rare for many language pairs. The first statistical machine translation software was [[CANDIDE]] from [[IBM]]. In 2005, Google improved its internal translation capabilities by using approximately 200 billion words from United Nations materials to train their system; translation accuracy improved.<ref>{{cite web |url=http://blog.outer-court.com/archive/2005-05-22-n83.html |title=Google Translator: The Universal Language |publisher=Blog.outer-court.com |date=25 January 2007 |access-date=2012-06-12 |archive-date=20 November 2008 |archive-url=https://web.archive.org/web/20081120030225/http://blog.outer-court.com/archive/2005-05-22-n83.html |url-status=live }}</ref> SMT's biggest downfall included it being dependent upon huge amounts of parallel texts, its problems with morphology-rich languages (especially with translating ''into'' such languages), and its inability to correct singleton errors. Some work has been done in the utilization of multiparallel [[text corpus|corpora]], that is a body of text that has been translated into 3 or more languages. Using these methods, a text that has been translated into 2 or more languages may be utilized in combination to provide a more accurate translation into a third language compared with if just one of those source languages were used alone.<ref>{{Cite conference |last=Schwartz |first=Lane |date=2008 |title=Multi-Source Translation Methods |url=https://dowobeha.github.io/papers/amta08.pdf |conference=Paper presented at the 8th Biennial Conference of the Association for Machine Translation in the Americas |archive-url=https://web.archive.org/web/20160629171944/http://dowobeha.github.io/papers/amta08.pdf |archive-date=29 June 2016 |access-date=3 November 2017 |url-status=live}}</ref><ref>{{Cite conference |last1=Cohn |first1=Trevor |last2=Lapata |first2=Mirella |date=2007 |title=Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora |url=http://homepages.inf.ed.ac.uk/mlap/Papers/acl07.pdf |conference=Paper presented at the 45th Annual Meeting of the Association for Computational Linguistics, June 23β30, 2007, Prague, Czech Republic |archive-url=https://web.archive.org/web/20151010171334/http://homepages.inf.ed.ac.uk/mlap/Papers/acl07.pdf |archive-date=10 October 2015 |access-date=3 February 2015 |url-status=live}}</ref><ref>{{Cite journal |last1=Nakov |first1=Preslav |last2=Ng |first2=Hwee Tou |date=2012 |title=Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages |url=https://jair.org/index.php/jair/article/view/10764 |journal=Journal of Artificial Intelligence Research |volume=44 |pages=179β222 |arxiv=1401.6876 |doi=10.1613/jair.3540 |doi-access=free}}</ref>
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