@inproceedings{delil-etal-2021-sefamerve,
    title = "Sefamerve {ARGE} at {S}em{E}val-2021 Task 5: Toxic Spans Detection Using Segmentation Based 1-{D} Convolutional Neural Network Model",
    author = {Delil, Selman  and
      Kuyumcu, Birol  and
      Aksakall{\i}, C{\"u}neyt},
    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/add-emnlp-2024-awards/2021.semeval-1.123/",
    doi = "10.18653/v1/2021.semeval-1.123",
    pages = "909--912",
    abstract = "This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection. Our approach considers toxic spans detection as a segmentation problem. The system, Waw-unet, consists of a 1-D convolutional neural network adopted from U-Net architecture commonly applied for semantic segmentation. We customize existing architecture by adding a special network block considering for text segmentation, as an essential component of the model. We compared the model with two transformers-based systems RoBERTa and XLM-RoBERTa to see its performance against pre-trained language models. We obtained 0.6251 f1 score with Waw-unet while 0.6390 and 0.6601 with the compared models respectively."
}