@inproceedings{yadav-singh-2025-dll5143a,
    title = "{D}ll5143{A}@{NLU} of {D}evanagari Script Languages 2025: Detection of Hate Speech and Targets Using Hierarchical Attention Network",
    author = "Yadav, Ashok  and
      Singh, Vrijendra",
    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_wac_2008/2025.chipsal-1.31/",
    pages = "278--288",
    abstract = "Hate speech poses a significant challenge on social networks, particularly in Devanagari scripted languages, where subtle expressions can lead to harmful narratives. This paper details our participation in the {\textquotedblleft}Shared Task on Natural Language Understanding of Devanagari Script Languages{\textquotedblright} at CHIPSAL@COLING 2025, addressing hate speech detection and target identification. In Sub-task B, we focused on classifying the text either hate or non-hate classified text to determine the presence of hate speech, while Sub-task C focused on identifying targets, such as individuals, organizations, or communities. We utilized the XLM-RoBERTa model as our base and explored various adaptations, including Adaptive Weighting and Gated Adaptive Weighting methods. Our results demonstrated that the Hierarchical Gated adaptive weighting model achieved 86{\%} accuracy in hate speech detection with a macro F1 score of 0.72, particularly improving performance for minority class detection. For target detection, the same model achieved 75{\%} accuracy and a 0.69 macro F1 score. Our proposed architecture demonstrated competitive performance, ranking 8th in Subtask B and 11th in Subtask C among all participants."
}