Pivot Language

Pivot language techniques leverage high-resource languages to improve machine translation, speech recognition, and instruction tuning for low-resource languages. Current research focuses on optimizing multi-source pivoting strategies, including ensemble methods that combine predictions from multiple pivot languages, and exploring the effectiveness of synthetic data augmentation. These methods aim to address the data scarcity challenges inherent in low-resource language processing, ultimately improving cross-lingual communication and access to technology for under-resourced communities. While using a single high-resource pivot (like English) often remains the most effective approach, ongoing work seeks to refine multi-pivot strategies and understand the optimal selection of pivot languages for specific tasks.