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

• Autores: John Evan Ortega, Weiyi Lu, Adam Meyers, Kyungyun Cho
• Localización: Proceedings of the 21st Annual Conference of the European Association for Machine Translation: 28-30 May 2018, Universitat d'Alacant, Alacant, Spain / coord. por Juan Antonio Pérez Ortiz, Felipe Sánchez Martínez, Miquel Esplà Gomis, Maja Popovic, Celia Rico Pérez, André Martins, Joachim Van den Bogaert, Mikel L. Forcada Zubizarreta, 2018, ISBN 978-84-09-01901-4, págs. 209-218
• Idioma: inglés
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• Resumen

  • 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.