Cross Lingual Transfer Learning

Cross-lingual transfer learning aims to leverage knowledge gained from high-resource languages to improve the performance of natural language processing (NLP) and speech technologies in low-resource languages. Current research focuses on optimizing multilingual model architectures, such as Transformers, and exploring techniques like prompt tuning and knowledge distillation to enhance cross-lingual transfer effectiveness across diverse tasks including machine translation, speech recognition, and sentiment analysis. This field is crucial for bridging the digital divide, enabling broader access to NLP and speech technologies and fostering more inclusive applications in areas like healthcare and education.