2020

Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages

Nekoto, Wilhelmina, Marivate, Vukosi, Matsila, Tshinondiwa et al.

Understand

Research in NLP lacks geographic diversity, and the question of how NLP can be scaled to low-resourced languages has not yet been adequately solved.

"Low-resourced"-ness is a complex problem going beyond data availability and reflects systemic problems in society.
In this paper, we focus on the task of Machine Translation (MT), that plays a crucial role for information accessibility and communication worldwide.
Despite immense improvements in MT over the past decade, MT is centered around a few high-resourced languages.

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