Multilingual Pretraining

Multilingual pretraining aims to build language models capable of understanding and generating text in multiple languages simultaneously, improving efficiency and performance compared to training separate models for each language. Current research focuses on optimizing pretraining objectives (e.g., language modeling, translation), exploring the impact of different model architectures (e.g., Transformers, RNNTs), and investigating strategies for effective cross-lingual transfer and handling low-resource languages. These advancements are significant for expanding access to natural language processing technologies across diverse linguistic communities and enabling applications like cross-lingual machine translation, speech recognition, and text-to-speech synthesis.