Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/76038
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dc.contributor.authorOrtega, John E.-
dc.contributor.authorLu, Weiyi-
dc.contributor.authorMeyers, Adam-
dc.contributor.authorCho, Kyunghyun-
dc.date.accessioned2018-05-30T12:42:14Z-
dc.date.available2018-05-30T12:42:14Z-
dc.date.issued2018-
dc.identifier.citationOrtega, John E., Lu, Weiyi; Meyers, Adam; Cho, Kyunghyun. “Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair”. In: Pérez-Ortiz, Juan Antonio, et al. (Eds.). Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain, pp. 209-218es_ES
dc.identifier.isbn978-84-09-01901-4-
dc.identifier.urihttp://hdl.handle.net/10045/76038-
dc.description.abstractWhile systems using the Neural Network-based Machine Translation (NMT) paradigm achieve the highest scores on recent shared tasks, phrase-based (PBMT) systems, rule-based (RBMT) systems and other systems may get better results for individual examples. Therefore, combined systems should achieve the best results for MT, particularly if the system combination method can take advantage of the strengths of each paradigm. In this paper, we describe a system that predicts whether a NMT, PBMT or RBMT will get the best Spanish translation result for a particular English sentence in DGT-TM 20161. Then we use fuzzy-match repair (FMR) as a mechanism to show that the combined system outperforms individual systems in a black-box machine translation setting.es_ES
dc.description.sponsorshipJohn E. Ortega is supported by the Universitat d’Alacant and the Spanish government through the EFFORTUNE (TIN2015-69632-R) project. Kyunghyun Cho was partly supported by Samsung Advanced Institute of Technology (Next Generation Deep Learning: from pattern recognition to AI) and Samsung Electronics (Improving Deep Learning using Latent Structure).es_ES
dc.languageenges_ES
dc.publisherEuropean Association for Machine Translationes_ES
dc.rights© 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.es_ES
dc.subjectMachine Translationes_ES
dc.subject.otherLenguajes y Sistemas Informáticoses_ES
dc.titleLetting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repaires_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.peerreviewedsies_ES
dc.relation.publisherversionhttp://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdfes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
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