Multilingual Transformer

Multilingual transformers are deep learning models designed to process and understand multiple languages simultaneously, aiming to overcome limitations of monolingual models and improve cross-lingual transferability. Current research focuses on evaluating their performance across diverse languages and tasks, particularly in low-resource settings, using architectures like mBERT, mT5, and XLM-RoBERTa, and exploring techniques like adapter modules and data augmentation to enhance efficiency and accuracy. This work is significant for advancing natural language processing capabilities in under-resourced languages and enabling broader access to applications like machine translation, summarization, and question answering across diverse linguistic contexts.