@inproceedings{ambaliya-etal-2025-niyamika,
    title = "Niyamika at {BHASHA} Task 1: Word-Level Transliteration for {E}nglish-{H}indi Mixed Text in Grammar Correction Using {MT}5",
    author = "Ambaliya, Rucha  and
      Dugar, Mahika  and
      Mishra, Pruthwik",
    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-1.13/",
    pages = "135--141",
    ISBN = "979-8-89176-313-5",
    abstract = "Grammar correction for Indian languages poses significant challenges due to complex morphology, non-standard spellings, and frequent script variations. In this work, we address grammar correction for English-mixed sentences in five Indic languages{---}Hindi, Bengali, Malayalam, Tamil, and Telugu{---}as part of the IndicGEC 2025 Bhasha Workshop. Our approach first applies word-level transliteration using IndicTrans (Bhat et al., 2014) to normalize Romanized and mixed-script tokens, followed by grammar correction using the mT5-small model (Xue et al., 2021). Although our experiments focus on these five languages, the methodology is generalizable to other Indian languages. Our implementation and code are publicly available at: https://github.com/Rucha-Ambaliya/bhasha-workshop"
}