Costa-jussà, Marta R.Farrús, Mireia2018-05-092018-05-092014Costa-Jussà MR, Farrús M. Statistical machine translation enhancements through linguistic levels: a survey. ACM Comput Surv. 2014; 46 (3): 42. DOI: 10.1145/25181300360-0300http://hdl.handle.net/10230/34599Machine translation can be considered a highly interdisciplinary and multidisciplinary field because it is approached from the point of view of human translators, engineers, computer scientists, mathematicians and linguists. One of the most popular approaches is the statistical machine translation (SMT) approach which tries to cover translation in a holistic manner by learning from parallel corpus aligned at the sentence level. However, with this basic approach, there are some issues at each written linguistic level (i.e. orthographic, morphological, lexical, syntactic and semantic) that remain unsolved. Research in SMT has continuously been focused on solving the different linguistic levels challenges. This paper represents a survey of how the SMT has been enhanced to perform translation correctly at all linguistic levels.application/pdfeng© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys (CSUR), 46(3) 2014. http://doi.acm.org/10.1145/2518130info:eu-repo/semantics/articlehttp://dx.doi.org/10.1145/2518130LinguisticsStatistical machine translationOrthographyMorphologyLexisSyntaxSemanticsinfo:eu-repo/semantics/openAccess