Lingual Transfer Capability

Lingual transfer capability research investigates how knowledge learned by language models in one language can be applied to another, particularly focusing on transferring capabilities from high-resource to low-resource languages. Current research explores this using various architectures, including transformer-based models like BERT and multilingual variations, as well as other neural network approaches, examining both the effectiveness of multilingual versus monolingual models and the impact of factors like data augmentation and fine-tuning techniques. This field is crucial for broadening the accessibility and effectiveness of natural language processing (NLP) tools across the world's diverse languages, impacting applications ranging from machine translation to code generation. The ability to efficiently transfer knowledge between languages is key to unlocking the potential of NLP for a wider range of users and tasks.