@inproceedings{chakrabarty-etal-2019-pay,
    title = "Pay ``Attention'' to your Context when Classifying Abusive Language",
    author = "Chakrabarty, Tuhin  and
      Gupta, Kilol  and
      Muresan, Smaranda",
    editor = "Roberts, Sarah T.  and
      Tetreault, Joel  and
      Prabhakaran, Vinodkumar  and
      Waseem, Zeerak",
    booktitle = "Proceedings of the Third Workshop on Abusive Language Online",
    month = aug,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W19-3508/",
    doi = "10.18653/v1/W19-3508",
    pages = "70--79",
    abstract = "The goal of any social media platform is to facilitate healthy and meaningful interactions among its users. But more often than not, it has been found that it becomes an avenue for wanton attacks. We propose an experimental study that has three aims: 1) to provide us with a deeper understanding of current data sets that focus on different types of abusive language, which are sometimes overlapping (racism, sexism, hate speech, offensive language, and personal attacks); 2) to investigate what type of attention mechanism (contextual vs. self-attention) is better for abusive language detection using deep learning architectures; and 3) to investigate whether stacked architectures provide an advantage over simple architectures for this task."
}