@inproceedings{k-etal-2021-amrita,
    title = "{A}mrita{\_}{CEN}{\_}{NLP}@{D}ravidian{L}ang{T}ech-{EACL}2021: Deep Learning-based Offensive Language Identification in {M}alayalam, {T}amil and {K}annada",
    author = "K, Sreelakshmi  and
      B, Premjith  and
      Kp, Soman",
    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/ingest-emnlp/2021.dravidianlangtech-1.34/",
    pages = "249--254",
    abstract = "This paper describes the submission of the team Amrita{\_}CEN{\_}NLP to the shared task on Offensive Language Identification in Dravidian Languages at EACL 2021. We implemented three deep neural network architectures such as a hybrid network with a Convolutional layer, a Bidirectional Long Short-Term Memory network (Bi-LSTM) layer and a hidden layer, a network containing a Bi-LSTM and another with a Bidirectional Recurrent Neural Network (Bi-RNN). In addition to that, we incorporated a cost-sensitive learning approach to deal with the problem of class imbalance in the training data. Among the three models, the hybrid network exhibited better training performance, and we submitted the predictions based on the same."
}