Neural POS Tagging for the Low-Resource Saraiki
Part-of-speech tagging is a fundamental sequence labeling task and an important component for many downstream natural language processing applications.
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Key points
- However, Saraiki, written from right to left in a Perso-Arabic script, remains an under-resourced Indo-Aryan language, despite being spoken as a mother tongue by 28,849,579 people (approximately 28.9 million) according to Pakistan 7th Population and Housing Census 2023.
- This paper presents a manually annotated Saraiki Universal POS tagging dataset developed from plain-text.
- We evaluate three pretrained transformer encoders under the same fine-tuning setup: XLM-RoBERTa base, Urdu RoBERTa small, and multilingual DistilBERT.
- The dataset, evaluation setup, and baseline results introduced in this work provide a benchmark for future research on Saraiki NLP and low-resource sequence labeling.
Sources (1)
- [1]Neural POS Tagging for the Low-Resource SaraikiarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 02:38 PM
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 an under-resourced Indo-Aryan language, despite being spoken as a mother tongue by 28,849,579 people (approximately 28.9 million) according to Pakistan 7th Population and Housing Census 2023.
Extractive summary: sentences quoted from the sources.
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