@article{malmasi-dras-2018-native,
    title = "Native Language Identification With Classifier Stacking and Ensembles",
    author = "Malmasi, Shervin  and
      Dras, Mark",
    journal = "Computational Linguistics",
    volume = "44",
    number = "3",
    month = sep,
    year = "2018",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/fix-sig-urls/J18-3003/",
    doi = "10.1162/coli_a_00323",
    pages = "403--446",
    abstract = "Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes."
}