Cross Lingual Model

Cross-lingual models aim to build language understanding systems capable of handling multiple languages simultaneously, overcoming data scarcity issues in low-resource languages. Current research focuses on improving cross-lingual transfer learning through techniques like knowledge distillation, multilingual instruction tuning, and contextual label projection, often employing transformer-based architectures like BERT and mT5. These advancements are significant for expanding access to NLP technologies across diverse linguistic communities and enabling applications like cross-lingual question answering, machine translation, and sentiment analysis in a wider range of languages.