2025
IJCNLP
IJCNLP 2025
Team Horizon at BHASHA Task 2: Fine-tuning Multilingual Transformers for Indic Word Grouping
Abstract
AbstractWe 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.
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Interdisciplinary Bridge
— Artificial Intelligence and Deep Learning and Machine Learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio