Investigating Effective Transfer Learning Strategies for Natural Language Processing Tasks in Low-Resource Languages

Abstract

This research investigates effective transfer learning strategies for Natural Language Processing (NLP) tasks in low-resource languages, focusing on the Pakistani context. Through a comprehensive methodology, including literature review, data collection, and experimentation with pre-trained models like mBERT and XLM-R, the study demonstrates the potential of transfer learning to enhance model performance for Urdu, Panjabi, Balochi, Pashto, and Sindhi. The findings reveal significant improvements in accuracy and F1 scores through fine-tuning and data augmentation. Qualitative analyses, including error inspection and user feedback, highlight the necessity for ongoing refinements in model usability, particularly concerning cultural nuances and real-time feedback mechanisms.