Cross Lingual Natural Language Understanding

Cross-lingual natural language understanding (XNLU) aims to enable computers to understand and process text in multiple languages, bridging the gap between languages with abundant data and those with limited resources. Current research focuses on leveraging multilingual pre-trained language models (MPLMs), often combined with techniques like self-distillation, data augmentation (including pseudo-semantic augmentation), and prompt-based fine-tuning, to improve zero-shot and few-shot cross-lingual transfer performance across various NLU tasks. These advancements are crucial for expanding the reach of NLP applications globally, particularly benefiting low-resource languages and enabling more inclusive access to technology.