Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation
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http://hdl.handle.net/10045/76037
Títol: | Evaluation of Terminology Translation in Instance-Based Neural MT Adaptation |
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Autors: | Farajian, M. Amin | Bertoldi, Nicola | Negri, Matteo | Turchi, Marco | Federico, Marcello |
Paraules clau: | Machine Translation |
Àrees de coneixement: | Lenguajes y Sistemas Informáticos |
Data de publicació: | 2018 |
Editor: | European Association for Machine Translation |
Citació bibliogràfica: | 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 |
Resum: | 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. |
Patrocinadors: | 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. |
URI: | http://hdl.handle.net/10045/76037 |
ISBN: | 978-84-09-01901-4 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/conferenceObject |
Drets: | © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND. |
Revisió científica: | si |
Versió de l'editor: | http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf |
Apareix a la col·lecció: | EAMT2018 - Proceedings Investigacions finançades per la UE |
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Arxiu | Descripció | Tamany | Format | |
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EAMT2018-Proceedings_17.pdf | 1,61 MB | Adobe PDF | Obrir Vista prèvia | |
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