@inproceedings{m-etal-2026-tamilmayangolispell,
    title = "{T}amil{M}ayangoli{S}pell: An Open-Source Neural Framework for Context-Sensitive Mayangoli Error Correction in {T}amil",
    author = "M, Yazhmozhi V  and
      Waller, Annalu  and
      Visser, Jacky",
    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-1.6/",
    pages = "42--51",
    ISBN = "979-8-89176-401-9",
    abstract = "Mayangoli errors are context-sensitive errors in Tamil that arise from confusion among phonetically similar graphemes (e.g., ல/ள/ழ, ர/ற, ந/ன/ண). These errors are challenging for conventional spell checkers because both incorrect and correct forms are valid dictionary words, making dictionary lookup insufficient and requiring contextual modelling. We present TamilMayangoliSpell, a reproducible framework for Mayangoli error correction that combines (i) Tamil-specific preprocessing for sentence segmentation and normalisation, (ii) linguistically grounded error induction for generating training data constrained by dictionary validity, and (iii) fine-tuning of multilingual sequence-to-sequence models. Using 30,000 sentence pairs derived from TamilCorp, a massive multi-genre Tamil corpus and split 80/10/10 into train/validation/test, we fine-tune mBART, mT5, and NLLB under a small hyperparameter grid using greedy decoding with a maximum sequence length of 128. mT5 achieves the best performance (BLEU 99.28; Exact Match Accuracy 93.50{\%}) and remains strong in a cross-genre evaluation on short stories. The preprocessing scripts, generated parallel datasets, and trained models are publicly available in a GitHub repository."
}