FastPOS: Language-Agnostic Scalable POS Tagging Framework Low-Resource Use Case
This addresses the problem of efficient NLP tool development for underrepresented languages, though it is incremental as it builds on existing transformer architectures.
The study tackled POS tagging for low-resource languages by proposing a language-agnostic transformer-based framework, achieving 96.85% and 97% token-level accuracy in Bangla and Hindi with minimal code adaptation.
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.