Multilingual Encoders

Multilingual encoders aim to create computational representations of text and speech that capture semantic meaning across multiple languages, facilitating cross-lingual understanding and applications. Current research focuses on improving these encoders' performance through techniques like dual encoders, contrastive learning, and knowledge distillation, often within transformer-based architectures. These advancements are crucial for bridging language barriers in various applications, including machine translation, speech recognition, and information retrieval, particularly benefiting low-resource languages. The development of more efficient and effective multilingual encoders is driving progress in numerous fields requiring cross-lingual capabilities.