Cross Lingual Language Model

Cross-lingual language models aim to build single models capable of understanding and generating text across multiple languages, overcoming limitations of monolingual models and resource scarcity for low-resource languages. Current research focuses on improving model architectures, such as employing expert models trained independently on subsets of multilingual data or leveraging contrastive learning to enhance cross-lingual alignment at various granularities, including token and sentence levels. These advancements are significantly impacting fields like machine translation, speech recognition, and sentiment analysis by enabling more efficient and effective multilingual natural language processing applications, particularly for low-resource languages.