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
Información del item - Informació de l'item - Item information
Title: Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair
Authors: Ortega, John E. | Lu, Weiyi | Meyers, Adam | Cho, Kyunghyun
Keywords: Machine Translation
Knowledge Area: Lenguajes y Sistemas Informáticos
Issue Date: 2018
Publisher: European Association for Machine Translation
Citation: 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
Abstract: 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.
Sponsor: 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
Language: eng
Type: info:eu-repo/semantics/conferenceObject
Rights: © 2018 The authors. This article is licensed under a Creative Commons 3.0 licence, no derivative works, attribution, CC-BY-ND.
Peer Review: si
Publisher version: http://eamt2018.dlsi.ua.es/proceedings-eamt2018.pdf
Appears in Collections:Congresos - EAMT2018 - Proceedings

Files in This Item:
Files in This Item:
File Description SizeFormat 
ThumbnailEAMT2018-Proceedings_23.pdf1,52 MBAdobe PDFOpen Preview


This item is licensed under a Creative Commons License Creative Commons