@inproceedings{bjerva-2016-byte,
    title = "Byte-based Language Identification with Deep Convolutional Networks",
    author = "Bjerva, Johannes",
    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/add-emnlp-2024-awards/W16-4816/",
    pages = "119--125",
    abstract = "We report on our system for the shared task on discriminating between similar languages (DSL 2016). The system uses only byte representations in a deep residual network (ResNet). The system, named ResIdent, is trained only on the data released with the task (closed training). We obtain 84.88{\%} accuracy on subtask A, 68.80{\%} accuracy on subtask B1, and 69.80{\%} accuracy on subtask B2. A large difference in accuracy on development data can be observed with relatively minor changes in our network`s architecture and hyperparameters. We therefore expect fine-tuning of these parameters to yield higher accuracies."
}