@inproceedings{renjit-idicula-2021-cusatnlp,
    title = "{CUSATNLP}@{D}ravidian{L}ang{T}ech-{EACL}2021:Language Agnostic Classification of Offensive Content in Tweets",
    author = "Renjit, Sara  and
      Idicula, Sumam Mary",
    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/add-emnlp-2024-awards/2021.dravidianlangtech-1.32/",
    pages = "236--242",
    abstract = "Identifying offensive information from tweets is a vital language processing task. This task concentrated more on English and other foreign languages these days. In this shared task on Offensive Language Identification in Dravidian Languages, in the First Workshop of Speech and Language Technologies for Dravidian Languages in EACL 2021, the aim is to identify offensive content from code mixed Dravidian Languages Kannada, Malayalam, and Tamil. Our team used language agnostic BERT (Bidirectional Encoder Representation from Transformers) for sentence embedding and a Softmax classifier. The language-agnostic representation based classification helped obtain good performance for all the three languages, out of which results for the Malayalam language are good enough to obtain a third position among the participating teams."
}