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dc.contributor.authorRivera Trigueros, Irene 
dc.date.accessioned2021-05-11T07:09:15Z
dc.date.available2021-05-11T07:09:15Z
dc.date.issued2021-04-10
dc.identifier.citationRivera-Trigueros, I. Machine translation systems and quality assessment: a systematic review. Lang Resources & Evaluation (2021). [https://doi.org/10.1007/s10579-021-09537-5]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/68448
dc.descriptionThis work was supported by the Spanish Ministry of Science, Innovation and Universities (MCIU) (RTI2018-093348-B-I00, FPU17/00667); the Spanish State Research Agency (AEI) (RTI2018- 093348-B-I00); and the European Regional Development Fund (ERDF) (RTI2018-093348-B-I00).es_ES
dc.description.abstractNowadays, in the globalised context in which we find ourselves, language barriers can still be an obstacle to accessing information. On occasions, it is impossible to satisfy the demand for translation by relying only in human translators, therefore, tools such as Machine Translation (MT) are gaining popularity due to their potential to overcome this problem. Consequently, research in this field is constantly growing and new MT paradigms are emerging. In this paper, a systematic literature review has been carried out in order to identify what MT systems are currently most employed, their architecture, the quality assessment procedures applied to determine how they work, and which of these systems offer the best results. The study is focused on the specialised literature produced by translation experts, linguists, and specialists in related fields that include the English-Spanish language combination. Research findings show that neural MT is the predominant paradigm in the current MT scenario, being Google Translator the most used system. Moreover, most of the analysed works used one type of evaluation-either automatic or human-to assess machine translation and only 22% of the works combined these two types of evaluation. However, more than a half of the works included error classification and analysis, an essential aspect for identifying flaws and improving the performance of MT systems.es_ES
dc.description.sponsorshipSpanish Ministry of Science, Innovation and Universities (MCIU) RTI2018-093348-B-I00 FPU17/00667es_ES
dc.description.sponsorshipSpanish State Research Agency (AEI) RTI2018-093348-B-I00es_ES
dc.description.sponsorshipEuropean Commission RTI2018-093348-B-I00es_ES
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectEvaluation es_ES
dc.subjectMachine translationes_ES
dc.subjectSystematic reviewes_ES
dc.subjectQuality es_ES
dc.titleMachine translation systems and quality assessment: a systematic reviewes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s10579-021-09537-5
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España