@inproceedings{bansal-etal-2021-iitk,
    title = "{IITK}@Detox at {S}em{E}val-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection",
    author = "Bansal, Archit  and
      Kaushik, Abhay  and
      Modi, Ashutosh",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.semeval-1.24/",
    doi = "10.18653/v1/2021.semeval-1.24",
    pages = "211--219",
    abstract = "In this work, we present our approach and findings for SemEval-2021 Task 5 - Toxic Spans Detection. The task{'}s main aim was to identify spans to which a given text{'}s toxicity could be attributed. The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution. Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges. Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques."
}