@inproceedings{ortega-etal-2018-letting,
title = "Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair",
author = "Ortega, John E. and
Lu, Weiyi and
Meyers, Adam and
Cho, Kyunghyun",
editor = "P{\'e}rez-Ortiz, Juan Antonio and
S{\'a}nchez-Mart{\'\i}nez, Felipe and
Espl{\`a}-Gomis, Miquel and
Popovi{\'c}, Maja and
Rico, Celia and
Martins, Andr{\'e} and
Van den Bogaert, Joachim and
Forcada, Mikel L.",
booktitle = "Proceedings of the 21st Annual Conference of the European Association for Machine Translation",
month = may,
year = "2018",
address = "Alicante, Spain",
url = "https://aclanthology.org/2018.eamt-main.21",
pages = "229--238",
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.",
}
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%0 Conference Proceedings
%T Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair
%A Ortega, John E.
%A Lu, Weiyi
%A Meyers, Adam
%A Cho, Kyunghyun
%Y Pérez-Ortiz, Juan Antonio
%Y Sánchez-Martínez, Felipe
%Y Esplà-Gomis, Miquel
%Y Popović, Maja
%Y Rico, Celia
%Y Martins, André
%Y Van den Bogaert, Joachim
%Y Forcada, Mikel L.
%S Proceedings of the 21st Annual Conference of the European Association for Machine Translation
%D 2018
%8 May
%C Alicante, Spain
%F ortega-etal-2018-letting
%X 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.
%U https://aclanthology.org/2018.eamt-main.21
%P 229-238
Markdown (Informal)
[Letting a Neural Network Decide Which Machine Translation System to Use for Black-Box Fuzzy-Match Repair](https://aclanthology.org/2018.eamt-main.21) (Ortega et al., EAMT 2018)
ACL