Zero Shot Cross Lingual

Zero-shot cross-lingual natural language processing (NLP) aims to build models capable of understanding and generating text in multiple languages without requiring training data for each target language. Current research focuses on leveraging multilingual pre-trained language models (like mT5, XLM-R, and others), exploring techniques such as in-context learning, prompt engineering, and data augmentation strategies (including code-switching and pseudo-semantic data generation) to improve cross-lingual transfer. This field is crucial for bridging the language gap in NLP applications, enabling broader access to information and technology across diverse linguistic communities.