Multilingual BERT

Multilingual BERT models, leveraging the Transformer architecture, aim to improve natural language processing tasks across multiple languages simultaneously, addressing the limitations of monolingual models and the scarcity of resources for low-resource languages. Current research focuses on enhancing multilingual BERT's performance in various applications, including machine translation, text classification, and sentiment analysis, often employing techniques like data augmentation, adversarial training, and multi-task learning to mitigate biases and improve cross-lingual transfer. These advancements have significant implications for bridging the language gap in NLP, enabling broader access to information and technological advancements across diverse linguistic communities.