Low Resource Translation

Low-resource translation focuses on developing machine translation systems for language pairs where parallel training data is scarce. Current research emphasizes techniques like data augmentation (including back-translation and GAN-based methods), leveraging multilingual models and cross-lingual embeddings to improve data quality and transfer knowledge from high-resource languages, and exploring optimal pivoting strategies in multi-pivot approaches. These advancements are crucial for bridging the language gap, enabling communication and access to information for speakers of under-resourced languages, and fostering broader multilingual computational linguistics research.