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.
This paper presents a manually annotated Saraiki Universal POS tagging dataset developed from plain-text.
We annotated the text with 17 POS categories.
We evaluate three pretrained transformer encoders under the same fine-tuning setup:
- XLM-RoBERTa base
- Urdu RoBERTa small
- multilingual DistilBERT
Experimental results over five random seeds show that XLM-RoBERTa base achieves the best performance, with 87.29% +/- 0.21% development accuracy and 88.60% +/- 0.21% test accuracy.
Urdu RoBERTa small and multilingual DistilBERT also provide competitive results, suggesting that multilingual and related-script pretrained encoders are effective for Saraiki UPOS tagging.
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.