Resource-Efficient Fine-Tuning for Dialect Identification

This research explores practical approaches to dialect identification, focusing on resource-efficient fine-tuning techniques, which is crucial for real-world deployment. The focus on efficiency suggests a pragmatic understanding of the constraints in building and deploying such models.

• •Focuses on fine-tuning, implying pre-trained models are utilized.
• •Highlights efficiency, important for computational and memory usage.
• •Addresses practical challenges of dialect identification.