@inproceedings{al-azzawi-etal-2023-nlp,
    title = "{NLP}-{LTU} at {S}em{E}val-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset",
    author = {Al-Azzawi, Sana  and
      Kov{\'a}cs, Gy{\"o}rgy  and
      Nilsson, Filip  and
      Adewumi, Tosin  and
      Liwicki, Marcus},
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.semeval-1.196",
    doi = "10.18653/v1/2023.semeval-1.196",
    pages = "1421--1427",
    abstract = "In this paper, we propose a methodology fortask 10 of SemEval23, focusing on detectingand classifying online sexism in social me-dia posts. The task is tackling a serious is-sue, as detecting harmful content on socialmedia platforms is crucial for mitigating theharm of these posts on users. Our solutionfor this task is based on an ensemble of fine-tuned transformer-based models (BERTweet,RoBERTa, and DeBERTa). To alleviate prob-lems related to class imbalance, and to improvethe generalization capability of our model, wealso experiment with data augmentation andsemi-supervised learning. In particular, fordata augmentation, we use back-translation, ei-ther on all classes, or on the underrepresentedclasses only. We analyze the impact of thesestrategies on the overall performance of thepipeline through extensive experiments. whilefor semi-supervised learning, we found thatwith a substantial amount of unlabelled, in-domain data available, semi-supervised learn-ing can enhance the performance of certainmodels. Our proposed method (for which thesource code is available on Github12) attainsan F 1-score of 0.8613 for sub-taskA, whichranked us 10th in the competition.",
}

In this paper, we propose a methodology fortask 10 of SemEval23, focusing on detectingand classifying online sexism in social me-dia posts. The task is tackling a serious is-sue, as detecting harmful content on socialmedia platforms is crucial for mitigating theharm of these posts on users. Our solutionfor this task is based on an ensemble of fine-tuned transformer-based models (BERTweet,RoBERTa, and DeBERTa). To alleviate prob-lems related to class imbalance, and to improvethe generalization capability of our model, wealso experiment with data augmentation andsemi-supervised learning. In particular, fordata augmentation, we use back-translation, ei-ther on all classes, or on the underrepresentedclasses only. We analyze the impact of thesestrategies on the overall performance of thepipeline through extensive experiments. whilefor semi-supervised learning, we found thatwith a substantial amount of unlabelled, in-domain data available, semi-supervised learn-ing can enhance the performance of certainmodels. Our proposed method (for which thesource code is available on Github12) attainsan F 1-score of 0.8613 for sub-taskA, whichranked us 10th in the competition.
Al-Azzawi, Sana Kov{\'a}cs, Gy{\"o}rgy Nilsson, Filip Adewumi, Tosin Liwicki, Marcus
NLP-LTU at SemEval-2023 Task 10: The Impact of Data Augmentation and Semi-Supervised Learning Techniques on Text Classification Performance on an Imbalanced Dataset