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New dataset and models advance NLP for low-resource Saraiki language

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new dataset and evaluation framework for part-of-speech (POS) tagging in the low-resource Saraiki language. Despite being spoken by nearly 29 million people in Pakistan, Saraiki has lacked sufficient NLP resources. The study fine-tuned three transformer encoders—XLM-RoBERTa base, Urdu RoBERTa small, and multilingual DistilBERT—on a manually annotated dataset. XLM-RoBERTa base achieved the highest accuracy, demonstrating the effectiveness of multilingual and related-script models for Saraiki NLP tasks. AI

IMPACT Enables further development of NLP tools for under-resourced languages like Saraiki, potentially benefiting millions of speakers.

RANK_REASON The cluster is about an academic paper presenting a new dataset and evaluation for a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and models advance NLP for low-resource Saraiki language

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The cluster is about an academic paper presenting a new dataset and evaluation for a low-resource language. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Usman Nawaz, Muhammad Junaid Iqbal, Muhammad Asaf, Mariam Munsif Mir, Faisal Rehman, Tahir Alyas, Muhammad Hasnain ·

    Neural POS Tagging for the Low-Resource Saraiki

    arXiv:2610.20279v1 Announce Type: new Abstract: Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications. However, Saraiki, written from right to left in a Perso-Arabic script, remains a…

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