# Enhancing NER Performance in Low-Resource Pakistani Languages using Cross-Lingual Data Augmentation

arXiv: https://arxiv.org/abs/2504.08792 · HTML: https://mlsanity.com/paper/2504.08792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 16 of 16)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models](https://mlsanity.com/paper/2303.10420/) (2023)
- [Glot500: Scaling Multilingual Corpora and Language Models to 500 Languages](https://mlsanity.com/paper/2305.12182/) (2023)
- [How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks](https://mlsanity.com/paper/2303.00293/) (2023)
- [SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages](https://mlsanity.com/paper/2406.10118/) (2024)
- [Frustratingly Easy Label Projection for Cross-lingual Transfer](https://mlsanity.com/paper/2211.15613/) (2022)
- [Universal NER: A Gold-Standard Multilingual Named Entity Recognition Benchmark](https://mlsanity.com/paper/2311.09122/) (2023)
- [mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning](https://mlsanity.com/paper/2308.09073/) (2023)
- [CoDa: Constrained Generation based Data Augmentation for Low-Resource NLP](https://mlsanity.com/paper/2404.00415/) (2024)
- [Constrained Decoding for Cross-lingual Label Projection](https://mlsanity.com/paper/2402.03131/) (2024)
- [IndiText Boost: Text Augmentation for Low Resource India Languages](https://mlsanity.com/paper/2401.13085/) (2024)
- [An Experimental Study on Data Augmentation Techniques for Named Entity Recognition on Low-Resource Domains](https://mlsanity.com/paper/2411.14551/) (2024)

## Built on by (most cited first, 2 of 2)

- [Framing Political Bias in Multilingual LLMs Across Pakistani Languages](https://mlsanity.com/paper/2506.00068/) (2025)
- [A Scalable Framework for Automated NER Annotation Correction in Low-Resource Languages](https://mlsanity.com/paper/2609.18739/) (2026)

## Abstract (arXiv)

Named Entity Recognition (NER), a fundamental task in Natural Language Processing (NLP), has shown significant advancements for high-resource languages. However, due to a lack of annotated datasets and limited representation in Pre-trained Language Models (PLMs), it remains understudied and challenging for low-resource languages. To address these challenges, we propose a data augmentation technique that generates culturally plausible sentences and experiments on four low-resource Pakistani languages; Urdu, Shahmukhi, Sindhi, and Pashto. By fine-tuning multilingual masked Large Language Models (LLMs), our approach demonstrates significant improvements in NER performance for Shahmukhi and Pashto. We further explore the capability of generative LLMs for NER and data augmentation using few-shot learning.