@inproceedings{gupta-etal-2025-iitr,
    title = "{IITR}-{CIOL}@{NLU} of {D}evanagari Script Languages 2025: Multilingual Hate Speech Detection and Target Identification in {D}evanagari-Scripted Languages",
    author = "Gupta, Siddhant  and
      Singhal, Siddh  and
      Wasi, Azmine Toushik",
    editor = "Sarveswaran, Kengatharaiyer  and
      Vaidya, Ashwini  and
      Krishna Bal, Bal  and
      Shams, Sana  and
      Thapa, Surendrabikram",
    booktitle = "Proceedings of the First Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2025)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2025.chipsal-1.33/",
    pages = "295--300",
    abstract = "This work focuses on two subtasks related to hate speech detection and target identification in Devanagari-scripted languages, specifically Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit. Subtask B involves detecting hate speech in online text, while Subtask C requires identifying the specific targets of hate speech, such as individuals, organizations, or communities. We develop a deep neural network built on the pretrained multilingual transformer model {\textquoteleft}ia-multilingual-transliterated-roberta' by IBM, optimized for classification tasks in multilingual and transliterated contexts. The model leverages contextualized embeddings to handle linguistic diversity, with a classifier head for binary classification. We received 88.40{\%} accuracy in Subtask B and 66.11{\%} accuracy in Subtask C, in the test set."
}