@inproceedings{raihan-etal-2023-offensive,
    title = "Offensive Language Identification in Transliterated and Code-Mixed {B}angla",
    author = "Raihan, Md Nishat  and
      Tanmoy, Umma  and
      Islam, Anika Binte  and
      North, Kai  and
      Ranasinghe, Tharindu  and
      Anastasopoulos, Antonios  and
      Zampieri, Marcos",
    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://aclanthology.org/2023.banglalp-1.1",
    doi = "10.18653/v1/2023.banglalp-1.1",
    pages = "1--6",
    abstract = "Identifying offensive content in social media is vital to create safe online communities. Several recent studies have addressed this problem by creating datasets for various languages. In this paper, we explore offensive language identification in texts with transliterations and code-mixing, linguistic phenomena common in multilingual societies, and a known challenge for NLP systems. We introduce TB-OLID, a transliterated Bangla offensive language dataset containing 5,000 manually annotated comments. We train and fine-tune machine learning models on TB-OLID, and we evaluate their results on this dataset. Our results show that English pre-trained transformer-based models, such as fBERT and HateBERT achieve the best performance on this dataset.",
}