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

TL;DR

This paper advocates for participatory research in low-resource machine translation, demonstrating its effectiveness through a case study on African languages, resulting in new datasets, benchmarks, and community involvement.

Contribution

It introduces participatory research as a scalable approach to develop MT resources for African languages, involving non-experts and producing open datasets and benchmarks.

Findings

Created translation datasets for over 30 African languages

Developed MT benchmarks with human evaluations for a third of these languages

Enabled community members without formal training to contribute scientifically

Abstract

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. As MT researchers cannot solve the problem of low-resourcedness alone, we propose participatory research as a means to involve all necessary agents required in the MT development process. We demonstrate the feasibility and scalability of participatory research with a case study on MT for African languages. Its implementation leads to a collection of novel translation datasets, MT benchmarks for over 30 languages, with human evaluations for a third of them, and enables participants without formal training to make a unique scientific contribution. Benchmarks, models, data, code, and evaluation results are released under https://github.com/masakhane-io/masakhane-mt.