@inproceedings{abuzayed-al-khalifa-2021-sarcasm,
    title = "Sarcasm and Sentiment Detection In {A}rabic Tweets Using {BERT}-based Models and Data Augmentation",
    author = "Abuzayed, Abeer  and
      Al-Khalifa, Hend",
    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/jlcl-multiple-ingestion/2021.wanlp-1.38/",
    pages = "312--317",
    abstract = "In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021). The shared task consists of two sub-tasks: Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). Our experiments were based on fine-tuning seven BERT-based models with data augmentation to solve the imbalanced data problem. For both tasks, the MARBERT BERT-based model with data augmentation outperformed other models with an increase of the F-score by 15{\%} for both tasks which shows the effectiveness of our approach."
}