African Language

Research on African languages is rapidly expanding, driven by the need to develop natural language processing (NLP) tools for the continent's diverse linguistic landscape. Current efforts focus on adapting and improving existing multilingual models (like BERT and HuBERT) and creating new, smaller, more efficient models specifically for low-resource African languages, often employing techniques like self-supervised learning and data augmentation to overcome data scarcity. This work is crucial for bridging the digital divide, enabling access to technology and information in local languages, and fostering linguistic diversity within the broader NLP community.