Multilingual Training

Multilingual training aims to improve the performance and inclusivity of machine learning models by training them on data from multiple languages simultaneously. Current research focuses on mitigating biases amplified by monolingual training, enhancing performance in low-resource languages through techniques like cross-lingual transfer learning and knowledge distillation, and optimizing model architectures (e.g., transformer-based models) for efficient multilingual processing. This approach is significant because it addresses data scarcity issues in many languages, leading to more robust and equitable AI systems across various applications, including text-to-speech, machine translation, and sentiment analysis.