@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://preview.aclanthology.org/Author-page-Marten-During-lu/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."
}