@inproceedings{cianflone-kosseim-2016-n,
    title = "N-gram and Neural Language Models for Discriminating Similar Languages",
    author = "Cianflone, Andre  and
      Kosseim, Leila",
    editor = {Nakov, Preslav  and
      Zampieri, Marcos  and
      Tan, Liling  and
      Ljube{\v{s}}i{\'c}, Nikola  and
      Tiedemann, J{\"o}rg  and
      Malmasi, Shervin},
    booktitle = "Proceedings of the Third Workshop on {NLP} for Similar Languages, Varieties and Dialects ({V}ar{D}ial3)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/fix-sig-urls/W16-4831/",
    pages = "243--250",
    abstract = "This paper describes our submission to the 2016 Discriminating Similar Languages (DSL) Shared Task. We participated in the closed Sub-task 1 with two separate machine learning techniques. The first approach is a character based Convolution Neural Network with an LSTM layer (CLSTM), which achieved an accuracy of 78.45{\%} with minimal tuning. The second approach is a character-based n-gram model of size 7. It achieved an accuracy of 88.45{\%} which is close to the accuracy of 89.38{\%} achieved by the best submission."
}