FastPOS: Language-Agnostic Scalable POS Tagging Framework Low-Resource Use Case

• URL: http://arxiv.org/abs/2512.00745v1
• Date: Sun, 30 Nov 2025 05:48:12 GMT
• Title: FastPOS: Language-Agnostic Scalable POS Tagging Framework Low-Resource Use Case
• Authors: Md Abdullah Al Kafi, Sumit Kumar Banshal,
• Abstract summary: The framework achieves 96.85 percent and 97 percent token-level accuracy across POS categories in Bangla and Hindi.<n>Its modular and open-source design enables rapid cross-lingual adaptation while reducing model design and tuning overhead.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study proposes a language-agnostic transformer-based POS tagging framework designed for low-resource languages, using Bangla and Hindi as case studies. With only three lines of framework-specific code, the model was adapted from Bangla to Hindi, demonstrating effective portability with minimal modification. The framework achieves 96.85 percent and 97 percent token-level accuracy across POS categories in Bangla and Hindi while sustaining strong F1 scores despite dataset imbalance and linguistic overlap. A performance discrepancy in a specific POS category underscores ongoing challenges in dataset curation. The strong results stem from the underlying transformer architecture, which can be replaced with limited code adjustments. Its modular and open-source design enables rapid cross-lingual adaptation while reducing model design and tuning overhead, allowing researchers to focus on linguistic preprocessing and dataset refinement, which are essential for advancing NLP in underrepresented languages.

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