@inproceedings{a-fondekar-etal-2024-unpacking,
    title = "Unpacking Faux-Hate: Addressing Faux-Hate Detection and Severity Prediction in Code-Mixed {H}inglish Text with {H}ing{R}o{BERT}a and Class Weighting Techniques",
    author = "A. Fondekar, Ashweta  and
      M. Shivolkar, Milind  and
      D. Pawar, Jyoti",
    editor = "Biradar, Shankar  and
      Reddy, Kasu Sai Kartheek  and
      Saumya, Sunil  and
      Akhtar, Md. Shad",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON): Shared Task on Decoding Fake Narratives in Spreading Hateful Stories (Faux-Hate)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.icon-fauxhate.2/",
    pages = "6--11",
    abstract = "The proliferation of hate speech and fake narra-tives on social media poses significant societalchallenges, especially in multilingual and code-mixed contexts. This paper presents our systemsubmitted to the ICON 2024 shared task onDecoding Fake Narratives in Spreading Hate-ful Stories (Faux-Hate). We tackle the prob-lem of Faux-Hate Detection, which involvesdetecting fake narratives and hate speech incode-mixed Hinglish text. Leveraging Hin-gRoBERTa, a pre-trained transformer modelfine-tuned on Hinglish datasets, we addresstwo sub-tasks: Binary Faux-Hate Detection andTarget and Severity Prediction. Through the in-troduction of class weighting techniques andthe optimization of a multi-task learning ap-proach, we demonstrate improved performancein identifying hate and fake speech, as well asin classifying their target and severity. Thisresearch contributes to a scalable and efficientframework for addressing complex real-worldtext processing challenges."
}