NLPGhana: A Case Study on Building A Self-Sustaining Machine Learning Research Ecosystem in Africa

Machine learning techniques for analyzing human language have traditionally focused on English, other European languages and widely-spoken Asian languages. While African languages have been getting more attention recently, the same forces that led to them being ignored in the first place are being replicated internally within Africa. Landmark studies in African Natural Language Processing (NLP) focus on widely spoken languages like Swahili, Yoruba, Amharic, Zulu, etc. Languages with fewer speakers, such as all Ghanaian languages, are relegated to the "Future Work" section and are rarely treated as a priority by researchers, research funding agencies or press. I will discuss NLPGhana, an initiative I co-founded to ensure that Ghanaian languages are treated as a priority in African NLP research. I will discuss some of our technical achievements - such as Khaya, the world's first and only Ghanaian language neural machine translation app that is already being used widely by the Ghanaian public. I will discuss challenges faced in trying to make this research ecosystem self-sustaining, given the aforementioned lack of support for languages with fewer speakers.