African languages continue to lag in natural language processing (NLP), Large Language Models (LLMS), and AI research, and this has been mainly due to the lack of quality datasets. The goal of the project is to increase the representation of African languages in NLP, LLMS, and AI research by creating a quality dataset with theoretically sound and consistent syntactic human annotations for eleven typologically diverse African languages.

In Spring 2024, our Postdoctoral fellow Happy Buzaaba with Christiane Fellbaum presented at the Princeton Language + Intelligence (PLI) Symposium 2024 on the “Infrastructure for African languages: African Universal Dependencies Treebanks.”