Resourced Language

Research on resourced languages focuses on developing and adapting natural language processing (NLP) techniques for languages with limited available data, addressing the significant imbalance in resources compared to dominant languages like English. Current efforts concentrate on leveraging techniques like transfer learning, multilingual models (often based on transformer architectures), and data augmentation strategies (including synthetic data generation) to improve performance in tasks such as speech recognition, machine translation, and hate speech detection. This work is crucial for bridging the digital divide and ensuring equitable access to NLP technologies, impacting fields ranging from social media monitoring to language preservation efforts.