@inproceedings{mishra-etal-2020-multilingual,
    title = "Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at {TRAC} 2020",
    author = "Mishra, Sudhanshu  and
      Prasad, Shivangi  and
      Mishra, Shubhanshu",
    editor = "Kumar, Ritesh  and
      Ojha, Atul Kr.  and
      Lahiri, Bornini  and
      Zampieri, Marcos  and
      Malmasi, Shervin  and
      Murdock, Vanessa  and
      Kadar, Daniel",
    booktitle = "Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.trac-1.19/",
    pages = "120--125",
    language = "eng",
    ISBN = "979-10-95546-56-6",
    abstract = "We present our team {\textquoteleft}3Idiots' (referred as {\textquoteleft}sdhanshu' in the official rankings) approach for the Trolling, Aggression and Cyberbullying (TRAC) 2020 shared tasks. Our approach relies on fine-tuning various Transformer models on the different datasets. We also investigated the utility of task label marginalization, joint label classification, and joint training on multilingual datasets as possible improvements to our models. Our team came second in English sub-task A, a close fourth in the English sub-task B and third in the remaining 4 sub-tasks. We find the multilingual joint training approach to be the best trade-off between computational efficiency of model deployment and model`s evaluation performance. We open source our approach at \url{https://github.com/socialmediaie/TRAC2020}."
}