Cross Lingual Alignment

Cross-lingual alignment focuses on creating meaningful similarities between language representations in multilingual language models, aiming to improve cross-lingual transfer learning and zero-shot capabilities. Current research explores techniques like contrastive learning, modular training of sentence encoders, and automatic alignment planning, often leveraging parallel or transliterated data, to enhance alignment and address issues like the "curse of multilinguality" and performance disparities across languages. These advancements are significant for improving the performance of multilingual NLP tasks, particularly benefiting low-resource languages and enabling more effective cross-lingual applications such as machine translation and question answering. The ultimate goal is to develop models that seamlessly handle multiple languages without sacrificing accuracy or efficiency.