@inproceedings{mukherjee-etal-2023-ufal,
    title = "{UFAL}-{ULD} at {BLP}-2023 Task 1: Violence Detection in {B}angla Text",
    author = "Mukherjee, Sourabrata  and
      Ojha, Atul Kr.  and
      Du{\v{s}}ek, Ond{\v{r}}ej",
    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-emnlp/2023.banglalp-1.27/",
    doi = "10.18653/v1/2023.banglalp-1.27",
    pages = "220--224",
    abstract = "In this paper, we present UFAL-ULD team{'}s system, desinged as a part of the BLP Shared Task 1: Violence Inciting Text Detection (VITD). This task aims to classify text, with a particular challenge of identifying incitement to violence into Direct, Indirect or Non-violence levels. We experimented with several pre-trained sequence classification models, including XLM-RoBERTa, BanglaBERT, Bangla BERT Base, and Multilingual BERT. Our best-performing model was based on the XLM-RoBERTa-base architecture, which outperformed the baseline models. Our system was ranked 20th among the 27 teams that participated in the task."
}