Language Dataset

Language datasets are crucial for training and evaluating natural language processing (NLP) models, with current research heavily focused on addressing the scarcity of high-quality data for low-resource languages. This involves developing techniques like cross-lingual transfer learning, employing novel data collection methods (e.g., storyboards), and designing model architectures such as Mixture-of-Experts (MoE) to mitigate catastrophic forgetting during multilingual training. These advancements are vital for broadening the reach of NLP applications and fostering inclusivity in the field by enabling the development of robust and accurate models for a wider range of languages.