@inproceedings{elaraby-zahran-2019-character,
    title = "A Character Level Convolutional {B}i{LSTM} for {A}rabic Dialect Identification",
    author = "Elaraby, Mohamed  and
      Zahran, Ahmed",
    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/iwcs-25-ingestion/W19-4636/",
    doi = "10.18653/v1/W19-4636",
    pages = "274--278",
    abstract = "In this paper, we describe CU-RAISA teamcontribution to the 2019Madar shared task2, which focused on Twitter User fine-grained dialect identification. Among par-ticipating teams, our system ranked the4th(with 61.54{\%}) F1-Macro measure. Our sys-tem is trained using a character level convo-lutional bidirectional long-short-term memorynetwork trained on 2k users' data. We showthat training on concatenated user tweets asinput is further superior to training on usertweets separately and assign user{'}s label on themode of user{'}s tweets' predictions."
}