Profanity and Offensiveness Detection in Nepali Language Using Bi-directional LSTM Models

@inproceedings{adhikari-etal-2024-profanity,
    title = "Profanity and Offensiveness Detection in {N}epali Language Using Bi-directional {LSTM} Models",
    author = "Adhikari, Abiral  and
      Manandhar, Prashant  and
      Khanal, Reewaj  and
      Wagle, Samir  and
      Acharya, Praveen  and
      Bal, Bal Krishna",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://aclanthology.org/2024.icon-1.60/",
    pages = "515--521",
    abstract = "Offensive and profane content has been on the rise in Nepali Social Media, which, is very disturbing to users. This is partly due to the absence of proper tools and mechanisms for the Nepali language to deal with profanity and offensive texts. In this work, we attempt to develop a deep learning-based profanity and offensive comments detection tool. We develop a Bi-LSTM (Bidirectional Long Short Term Memory) based model for the classification of Profane and Offensive comments and study different variations of the task. Furthermore, Multilingual BERT embedding and vocab embedding were used among others for an accurate understanding of the intent and decency of the posts. While previous related studies in the Nepali language are more focused on sentiment and offensiveness detection only, our study explores profanity and offensiveness detection as two distinct tasks. Our Bi-LSTM model outputs 87.8{\%} accuracy for"
}