@inproceedings{mohammed-kamakshi-2026-flaicol,
    title = "{FLAICOL}: Flip-Point-Led Augmentation for Imbalanced Code-Mixed Offensive Language Detection",
    author = "Mohammed, Danish  and
      Kamakshi, Vidhya",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.4/",
    pages = "21--31",
    ISBN = "979-8-89176-401-9",
    abstract = "Hate speech detection in low-resource, code-mixed languages is a challenging task as people often switch between scripts and languages in a single post. Code-Mixed scripts can take the form of explicit slurs, subtle insults, or fragmented abuse, and is often hidden by spelling variants and Romanized script. These datasets are also subjected to class imbalance with hate speech being a minority class of interest. To mitigate the imbalance, targeted data augmentation of minority class samples can help learn better representations to aid hate speech detection despite the naturally expected imbalance. We propose FLAICOL, a flip-point method which identifies the minimal embedding perturbation that moves an input across the decision boundary, map it back to discrete text, and retrain on those focused examples. Empirical results show that these interpretable augmentations strengthen Transformer classifiers on low-resource, code-mixed low resource hate datasets (Experiments were conducted on the Tamil-English, Malayalam-English, and Kannada-English splits in the Dravidian CodeMix Benchmark)."
}