Cross Lingual Representation

Cross-lingual representation focuses on enabling natural language processing (NLP) models to understand and process multiple languages effectively, bridging the gap between high-resource and low-resource languages. Current research emphasizes developing multilingual models, often leveraging transformer architectures and techniques like knowledge distillation, prompt tuning, and unsupervised data augmentation, to improve zero-shot cross-lingual transfer capabilities. This area is crucial for expanding NLP applications globally, particularly benefiting low-resource languages and facilitating efficient multilingual systems in diverse fields like machine translation, question answering, and information retrieval. The development of robust cross-lingual representations is driving advancements in both theoretical understanding of multilingual language processing and practical applications across various domains.