@inproceedings{ziehe-etal-2021-gcdh,
    title = "{GCDH}@{LT}-{EDI}-{EACL}2021: {XLM}-{R}o{BERT}a for Hope Speech Detection in {E}nglish, {M}alayalam, and {T}amil",
    author = "Ziehe, Stefan  and
      Pannach, Franziska  and
      Krishnan, Aravind",
    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-1.19/",
    pages = "132--135",
    abstract = "This paper describes approaches to identify Hope Speech in short, informal texts in English, Malayalam and Tamil using different machine learning techniques. We demonstrate that even very simple baseline algorithms perform reasonably well on this task if provided with enough training data. However, our best performing algorithm is a cross-lingual transfer learning approach in which we fine-tune XLM-RoBERTa."
}