@inproceedings{abdel-salam-2022-reamtchka,
    title = "reamtchka at {S}em{E}val-2022 Task 6: Investigating the effect of different loss functions for Sarcasm detection for unbalanced datasets",
    author = "Abdel-Salam, Reem",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.semeval-1.126/",
    doi = "10.18653/v1/2022.semeval-1.126",
    pages = "896--906",
    abstract = "This paper describes the system used in SemEval-2022 Task 6: Intended Sarcasm Detection in English and Arabic. Achieving 20th,3rd places with 34{\&} 47 F1-Sarcastic score for task A, 16th place for task B with 0.0560 F1-macro score, and 10, 6th places for task C with72{\%} and 80{\%} accuracy on the leaderboard. A voting classifier between either multiple different BERT-based models or machine learningmodels is proposed, as our final model. Multiple key points has been extensively examined to overcome the problem of the unbalance ofthe dataset as: type of models, suitable architecture, augmentation, loss function, etc. In addition to that, we present an analysis of ourresults in this work, highlighting its strengths and shortcomings."
}