@inproceedings{de-souza-etal-2014-towards,
    title = "Towards a combination of online and multitask learning for {MT} quality estimation: a preliminary study",
    author = "de Souza, Jos{\'e} G.C.  and
      Turchi, Marco  and
      Negri, Matteo",
    editor = "Casacuberta, Francisco  and
      Federico, Marcello  and
      Koehn, Philipp",
    booktitle = "Workshop on interactive and adaptive machine translation",
    month = oct # " 22",
    year = "2014",
    address = "Vancouver, Canada",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://aclanthology.org/2014.amta-workshop.2",
    pages = "9--19",
    abstract = "Quality estimation (QE) for machine translation has emerged as a promising way to provide real-world applications with methods to estimate at run-time the reliability of automatic translations. Real-world applications, however, pose challenges that go beyond those of current QE evaluation settings. For instance, the heterogeneity and the scarce availability of training data might contribute to significantly raise the bar. To address these issues we compare two alternative machine learning paradigms, namely online and multi-task learning, measuring their capability to overcome the limitations of current batch methods. The results of our experiments, which are carried out in the same experimental setting, demonstrate the effectiveness of the two methods and suggest their complementarity. This indicates, as a promising research avenue, the possibility to combine their strengths into an online multi-task approach to the problem.",
}