@inproceedings{ramesh-rajalakshmi-2025-dlrg,
    title = "{DLRG} at {BHASHA}: Task 1 ({I}ndic{GEC}): A Hybrid Neurosymbolic Approach for {T}amil and {M}alayalam Grammatical Error Correction",
    author = "Ramesh, Akshay  and
      Rajalakshmi, Ratnavel",
    editor = "Bhattacharya, Arnab  and
      Goyal, Pawan  and
      Ghosh, Saptarshi  and
      Ghosh, Kripabandhu",
    booktitle = "Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.bhasha-1.16/",
    pages = "155--163",
    ISBN = "979-8-89176-313-5",
    abstract = "Grammatical Error Correction (GEC) for low-resource Indic languages remains challenging due to limited annotated data and morphological complexity. We present a hybrid neurosymbolic GEC system that combines neural sequence-to-sequence models with explicit language-specific rule-based pattern matching. Our approach leverages parameter-efficient LoRA adaptation on aggressively augmented data to fine-tune pre-trained mT5 models, followed by learned correction rules through intelligent ensemble strategies. The proposed hybrid architecture achieved 85.34{\%} GLEU for Tamil (Rank 8) and 95.06{\%} GLEU for Malayalam (Rank 2) on the provided IndicGEC test sets, outperforming individual neural and rule-based approaches. The system incorporates conservative safety mechanisms to prevent catastrophic deletions and over-corrections, thus ensuring robustness and real-world applicability. Our work demonstrates that extremely low-resource GEC can be effectively addressed by combining neural generalization with symbolic precision."
}