@inproceedings{dowlagar-mamidi-2021-edione,
    title = "{EDIO}ne@{LT}-{EDI}-{EACL}2021: Pre-trained Transformers with Convolutional Neural Networks for Hope Speech Detection.",
    author = "Dowlagar, Suman  and
      Mamidi, Radhika",
    editor = "Chakravarthi, Bharathi Raja  and
      McCrae, John P.  and
      Zarrouk, Manel  and
      Bali, Kalika  and
      Buitelaar, Paul",
    booktitle = "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion",
    month = apr,
    year = "2021",
    address = "Kyiv",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.ltedi-1.11/",
    pages = "86--91",
    abstract = "Hope is an essential aspect of mental health stability and recovery in every individual in this fast-changing world. Any tools and methods developed for detection, analysis, and generation of hope speech will be beneficial. In this paper, we propose a model on hope-speech detection to automatically detect web content that may play a positive role in diffusing hostility on social media. We perform the experiments by taking advantage of pre-processing and transfer-learning models. We observed that the pre-trained multilingual-BERT model with convolution neural networks gave the best results. Our model ranked first, third, and fourth ranks on English, Malayalam-English, and Tamil-English code-mixed datasets."
}