@inproceedings{alharbi-lee-2021-multi,
    title = "Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for {A}rabic Sarcasm Detection and Sentiment Analysis",
    author = "Alharbi, Abdullah I.  and
      Lee, Mark",
    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/add-emnlp-2024-awards/2021.wanlp-1.39/",
    pages = "318--322",
    abstract = "Sarcasm detection and sentiment analysis are important tasks in Natural Language Understanding. Sarcasm is a type of expression where the sentiment polarity is flipped by an interfering factor. In this study, we exploited this relationship to enhance both tasks by proposing a multi-task learning approach using a combination of static and contextualised embeddings. Our proposed system achieved the best result in the sarcasm detection subtask."
}