@inproceedings{de-francony-etal-2019-hierarchical,
    title = "Hierarchical Deep Learning for {A}rabic Dialect Identification",
    author = "de Francony, Gael  and
      Guichard, Victor  and
      Joshi, Praveen  and
      Afli, Haithem  and
      Bouchekif, Abdessalam",
    editor = "El-Hajj, Wassim  and
      Belguith, Lamia Hadrich  and
      Bougares, Fethi  and
      Magdy, Walid  and
      Zitouni, Imed  and
      Tomeh, Nadi  and
      El-Haj, Mahmoud  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/W19-4631/",
    doi = "10.18653/v1/W19-4631",
    pages = "249--253",
    abstract = "In this paper, we present two approaches for Arabic Fine-Grained Dialect Identification. The first approach is based on Recurrent Neural Networks (BLSTM, BGRU) using hierarchical classification. The main idea is to separate the classification process for a sentence from a given text in two stages. We start with a higher level of classification (8 classes) and then the finer-grained classification (26 classes). The second approach is given by a voting system based on Naive Bayes and Random Forest. Our system achieves an F1 score of 63.02 {\%} on the subtask evaluation dataset."
}