Letting a neural network decide which machine translation system to use for black-box fuzzy-match repair

John E. Ortega, Weiyi Lu, Adam Meyers, Kyunghyun Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationEAMT 2018 - Proceedings of the 21st Annual Conference of the European Association for Machine Translation
EditorsJuan Antonio Perez-Ortiz, Felipe Sanchez-Martinez, Miquel Espla-Gomis, Maja Popovic, Celia Rico, Andre Martins, Joachim Van den Bogaert, Mikel L. Forcada
PublisherEuropean Association for Machine Translation
Pages209-218
Number of pages10
ISBN (Electronic)9788409019014
StatePublished - Jan 1 2018
Event21st Annual Conference of the European Association for Machine Translation, EAMT 2018 - Alacant, Spain
Duration: May 28 2018May 30 2018

Publication series

NameEAMT 2018 - Proceedings of the 21st Annual Conference of the European Association for Machine Translation

Conference

Conference21st Annual Conference of the European Association for Machine Translation, EAMT 2018
CountrySpain
CityAlacant
Period5/28/185/30/18

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software
  • Language and Linguistics

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  • Cite this

    Ortega, J. E., Lu, W., Meyers, A., & Cho, K. (2018). Letting a neural network decide which machine translation system to use for black-box fuzzy-match repair. In J. A. Perez-Ortiz, F. Sanchez-Martinez, M. Espla-Gomis, M. Popovic, C. Rico, A. Martins, J. Van den Bogaert, & M. L. Forcada (Eds.), EAMT 2018 - Proceedings of the 21st Annual Conference of the European Association for Machine Translation (pp. 209-218). (EAMT 2018 - Proceedings of the 21st Annual Conference of the European Association for Machine Translation). European Association for Machine Translation.