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- Abdallah Bashir
- Adewale Akinfaderin
- Alp Öktem
- Arshath Ramkilowan
- Ayodele Olabiyi
- Blessing Bassey
- Blessing Sibanda
- Bonaventure Dossou
- Chris Emezue
- Christopher Onyefuluchi
- Daniel Whitenack
- Elan van Biljon
- Espoir Murhabazi
- Ghollah Kioko
- Goodness Duru
- Hady Elsahar
- Herman Kamper
- Idris Abdulkadir Dangana
- Ignatius Ezeani
- Iroro Orife
- Jade Abbott
- Jamiil Toure Ali
- Julia Kreutzer
- Kathleen Siminyu
- Kelechi Ogueji
- Kevin Degila
- Kolawole Tajudeen
- Laura Jane Martinus
- Lawrence Okegbemi
- Masabata Mokgesi-Selinga
- Mofetoluwa Adeyemi
- Musie Meressa
- Orevaoghene Ahia
- Perez Ogayo
- Ricky Macharm
- Rubungo Andre Niyongabo
- Sackey Freshia
- Salomey Osei
- Salomon Kabongo Kabenamualu
- Shamsuddeen Muhammad
- Solomon Oluwole Akinola
- Taiwo Fagbohungbe
- Timi Fasubaa
- Tshinondiwa Matsila
- Vukosi Marivate
- Wilhelmina Nekoto
- Wole Akin
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 on GitHub.
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