Lanfrica: A Participatory Approach to Documenting Machine Translation Research on African Languages

TL;DR

Lanfrica is a participatory framework leveraging online platforms to document and share machine translation research, datasets, and benchmarks for African languages, enhancing accessibility and reproducibility in this highly diverse linguistic landscape.

Contribution

The paper introduces Lanfrica, a novel participatory framework that systematically documents African language MT research using online community engagement.

Findings

Lanfrica successfully aggregates diverse African language MT research.

The framework improves accessibility to datasets and benchmarks.

It fosters community participation in documenting linguistic resources.

Abstract

Over the years, there have been campaigns to include the African languages in the growing research on machine translation (MT) in particular, and natural language processing (NLP) in general. Africa has the highest language diversity, with 1500-2000 documented languages and many more undocumented or extinct languages(Lewis, 2009; Bendor-Samuel, 2017). This makes it hard to keep track of the MT research, models and dataset that have been developed for some of them. As the internet and social media make up the daily lives of more than half of the world(Lin, 2020), as well as over 40% of Africans(Campbell, 2019), online platforms can be useful in creating accessibility to researches, benchmarks and datasets in these African languages, thereby improving reproducibility and sharing of existing research and their results. In this paper, we introduce Lanfrica, a novel, on-going framework that employs a participatory approach to documenting researches, projects, benchmarks and dataset on African languages.