@inproceedings{algotiml-etal-2019-arabic,
    title = "{A}rabic Tweet-Act: Speech Act Recognition for {A}rabic Asynchronous Conversations",
    author = "Algotiml, Bushra  and
      Elmadany, AbdelRahim  and
      Magdy, Walid",
    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/W19-4620/",
    doi = "10.18653/v1/W19-4620",
    pages = "183--191",
    abstract = "Speech acts are the actions that a speaker intends when performing an utterance within conversations. In this paper, we proposed speech act classification for asynchronous conversations on Twitter using multiple machine learning methods including SVM and deep neural networks. We applied the proposed methods on the ArSAS tweets dataset. The obtained results show that superiority of deep learning methods compared to SVMs, where Bi-LSTM managed to achieve an accuracy of 87.5{\%} and a macro-averaged F1 score 61.5{\%}. We believe that our results are the first to be reported on the task of speech-act recognition for asynchronous conversations on Arabic Twitter."
}