Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation
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Campo DC | Valor | Idioma |
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dc.contributor.author | Farajian, M. Amin | - |
dc.contributor.author | Bertoldi, Nicola | - |
dc.contributor.author | Negri, Matteo | - |
dc.contributor.author | Turchi, Marco | - |
dc.contributor.author | Federico, Marcello | - |
dc.date.accessioned | 2018-05-30T12:29:06Z | - |
dc.date.available | 2018-05-30T12:29:06Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Farajian, M. Amin, et al. “Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation”. 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. 149-158 | es_ES |
dc.identifier.isbn | 978-84-09-01901-4 | - |
dc.identifier.uri | http://hdl.handle.net/10045/76037 | - |
dc.description.abstract | We address the issues arising when a neural machine translation engine trained on generic data receives requests from a new domain that contains many specific technical terms. Given training data of the new domain, we consider two alternative methods to adapt the generic system: corpus-based and instance-based adaptation. While the first approach is computationally more intensive in generating a domain-customized network, the latter operates more efficiently at translation time and can handle on-the-fly adaptation to multiple domains. Besides evaluating the generic and the adapted networks with conventional translation quality metrics, in this paper we focus on their ability to properly handle domain-specific terms. We show that instance-based adaptation, by fine-tuning the model on-the-fly, is capable to significantly boost the accuracy of translated terms, producing translations of quality comparable to the expensive corpus-based method. | es_ES |
dc.description.sponsorship | This work has been partially supported by the EC-funded H2020 projects QT21 (grant no. 645452) and ModernMT (grant no. 645487). This work was also supported by The Alan Turing Institute under the EPSRC grant EP/N510129/1 and by a donation of Azure credits by Microsoft. | es_ES |
dc.language | eng | es_ES |
dc.publisher | European Association for Machine Translation | es_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.subject | Machine Translation | es_ES |
dc.subject.other | Lenguajes y Sistemas Informáticos | es_ES |
dc.title | Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.peerreviewed | si | es_ES |
dc.relation.publisherversion | http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/645452 | es_ES |
Aparece en las colecciones: | EAMT2018 - Proceedings Investigaciones financiadas por la UE |
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