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

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Títol: Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair
Autors: Ortega, John E. | Lu, Weiyi | Meyers, Adam | Cho, Kyunghyun
Paraules clau: Machine Translation
Àrees de coneixement: Lenguajes y Sistemas Informáticos
Data de publicació: 2018
Editor: European Association for Machine Translation
Citació bibliogràfica: Ortega, 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. 209-218
Resum: While 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.
Patrocinadors: John 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).
URI: http://hdl.handle.net/10045/76038
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
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