@inproceedings{dhamecha-etal-2025-team-horizon,
    title = "Team Horizon at {BHASHA} Task 2: Fine-tuning Multilingual Transformers for {I}ndic Word Grouping",
    author = "Dhamecha, Manav  and
      Damor, Gaurav  and
      Jaat, Sunil  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-1.18/",
    pages = "175--179",
    ISBN = "979-8-89176-313-5",
    abstract = "We present Team Horizon{'}s approach to BHASHA Task 2: Indic Word Grouping. We model the word-grouping problem as token classification problem and fine-tune multilingual Transformer encoders for the task. We evaluated MuRIL, XLM-Roberta, and IndicBERT v2 and report Exact Match accuracy on the test data. Our best model (MuRIL) achieves 58.1818{\%} exact match on the test set."
}