@inproceedings{chen-kong-2021-cs,
    title = "cs@{D}ravidian{L}ang{T}ech-{EACL}2021: Offensive Language Identification Based On Multilingual {BERT} Model",
    author = "Chen, Shi  and
      Kong, Bing",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Kumar M, Anand  and
      Krishnamurthy, Parameswari  and
      Sherly, Elizabeth",
    booktitle = "Proceedings of the First Workshop on Speech and Language Technologies for Dravidian Languages",
    month = apr,
    year = "2021",
    address = "Kyiv",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.dravidianlangtech-1.31/",
    pages = "230--235",
    abstract = "This paper introduces the related content of the task {\textquotedblleft}Offensive Language Identification in Dravidian LANGUAGES-EACL 2021{\textquotedblright}. The task requires us to classify Dravidian languages collected from social media into Not-Offensive, Off-Untargeted, Off-Target-Individual, etc. This data set contains actual annotations in code-mixed text posted by users on Youtube, not from the monolingual text in textbooks. Based on the features of the data set code mixture, we use multilingual BERT and TextCNN for semantic extraction and text classification. In this article, we will show the experiment and result analysis of this task."
}