@inproceedings{chatterjee-etal-2023-vaclm,
    title = "{V}ac{LM} at {BLP}-2023 Task 1: Leveraging {BERT} models for Violence detection in {B}angla",
    author = "Chatterjee, Shilpa  and
      Evenss, P J Leo  and
      Bhattacharyya, Pramit",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Sadeque, Farig  and
      Amin, Ruhul",
    booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2023.banglalp-1.23/",
    doi = "10.18653/v1/2023.banglalp-1.23",
    pages = "196--200",
    abstract = "This study introduces the system submitted to the BLP Shared Task 1: Violence Inciting Text Detection (VITD) by the VacLM team. In this work, we analyzed the impact of various transformer-based models for detecting violence in texts. BanglaBERT outperforms all the other competing models. We also observed that the transformer-based models are not adept at classifying Passive Violence and Direct Violence class but can better detect violence in texts, which was the task`s primary objective. On the shared task, we secured a rank of 12 with macro F1-score of 72.656{\%}."
}