@inproceedings{abdel-salam-2021-wanlp,
    title = "{WANLP} 2021 Shared-Task: Towards Irony and Sentiment Detection in {A}rabic Tweets using Multi-headed-{LSTM}-{CNN}-{GRU} and {M}a{RBERT}",
    author = "Abdel-Salam, Reem",
    editor = "Habash, Nizar  and
      Bouamor, Houda  and
      Hajj, Hazem  and
      Magdy, Walid  and
      Zaghouani, Wajdi  and
      Bougares, Fethi  and
      Tomeh, Nadi  and
      Abu Farha, Ibrahim  and
      Touileb, Samia",
    booktitle = "Proceedings of the Sixth Arabic Natural Language Processing Workshop",
    month = apr,
    year = "2021",
    address = "Kyiv, Ukraine (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2021.wanlp-1.37/",
    pages = "306--311",
    abstract = "Irony and Sentiment detection is important to understand people{'}s behavior and thoughts. Thus it has become a popular task in natural language processing (NLP). This paper presents results and main findings in WANLP 2021 shared tasks one and two. The task was based on the ArSarcasm-v2 dataset (Abu Farha et al., 2021). In this paper, we describe our system Multi-headed-LSTM-CNN-GRU and also MARBERT (Abdul-Mageed et al., 2021) submitted for the shared task, ranked 10 out of 27 in shared task one achieving 0.5662 F1-Sarcasm and ranked 3 out of 22 in shared task two achieving 0.7321 F1-PN under CodaLab username ``rematchka''. We experimented with various models and the two best performing models are a Multi-headed CNN-LSTM-GRU in which we used prepossessed text and emoji presented from tweets and MARBERT."
}