Publication . Conference object . Article . Preprint . 2020

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

Wilhelmina Nekoto; Vukosi Marivate; Tshinondiwa Matsila; Timi E. Fasubaa; Tajudeen Kolawole; Taiwo Fagbohungbe; Solomon Oluwole Akinola; +41 Authors

Open Access

doi: 10.18653/v1/2020.findings-emnlp.195 , 10.48550/arxiv.2010.02353

Published: 05 Oct 2020

Publisher: Association for Computational Linguistics

Country: United Kingdom

- Summary
- References
  (74)
- Related research
  (1)

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.

Comment: Findings of EMNLP 2020; updated benchmarks

Subjects by Vocabulary

Microsoft Academic Graph classification: Focus (linguistics) Data science Task (project management) Participatory action research Languages of Africa Machine translation computer.software_genre computer Process (engineering) Computer science

Subjects

Computation and Language (cs.CL), Artificial Intelligence (cs.AI), Machine Learning (cs.LG), FOS: Computer and information sciences, Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning

Related Organizations

Sapienza University of Rome
Italy
Federal University of Technology Owerri
Nigeria
University of Hannover
Germany
Technische Universität Munchen
Finland
Jomo Kenyatta University of Agriculture and Technology
Kenya

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