@inproceedings{tofa-etal-2025-cuet,
    title = "{CUET}{\_}{INS}ights@{NLU} of {D}evanagari Script Languages 2025: Leveraging Transformer-based Models for Target Identification in Hate Speech",
    author = "Tofa, Farjana Alam  and
      Zeba, Lorin Tasnim  and
      Osama, Md  and
      Dey, Ashim",
    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/ingest-emnlp/2025.chipsal-1.29/",
    pages = "267--272",
    abstract = "Hate speech detection in multilingual content is a challenging problem especially when it comes to understanding the specific targets of hateful expressions. Identifying the targets of hate speech whether directed at individuals, organizations or communities is crucial for effective content moderation and understanding the context. A shared task on hate speech detection in Devanagari Script Languages organized by CHIPSAL@COLING 2025 allowed us to address the challenge of identifying the target of hate speech in the Devanagari Script Language. For this task, we experimented with various machine learning (ML) and deep learning (DL) models including Logistic Regression, Decision Trees, Random Forest, SVM, CNN, LSTM, BiLSTM, and transformer-based models like MiniLM, m-BERT, and Indic-BERT. Our experiments demonstrated that Indic-BERT achieved the highest F1-score of 0.69, ranked 3rd in the shared task. This research contributes to advancing the field of hate speech detection and natural language processing in low-resource languages."
}