%0 Conference Proceedings %T Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages %A Nekoto, Wilhelmina %A Marivate, Vukosi %A Matsila, Tshinondiwa %A Fasubaa, Timi %A Fagbohungbe, Taiwo %A Akinola, Solomon Oluwole %A Muhammad, Shamsuddeen %A Kabongo Kabenamualu, Salomon %A Osei, Salomey %A Sackey, Freshia %A Niyongabo, Rubungo Andre %A Macharm, Ricky %A Ogayo, Perez %A Ahia, Orevaoghene %A Berhe, Musie Meressa %A Adeyemi, Mofetoluwa %A Mokgesi-Selinga, Masabata %A Okegbemi, Lawrence %A Martinus, Laura %A Tajudeen, Kolawole %A Degila, Kevin %A Ogueji, Kelechi %A Siminyu, Kathleen %A Kreutzer, Julia %A Webster, Jason %A Ali, Jamiil Toure %A Abbott, Jade %A Orife, Iroro %A Ezeani, Ignatius %A Dangana, Idris Abdulkadir %A Kamper, Herman %A Elsahar, Hady %A Duru, Goodness %A Kioko, Ghollah %A Espoir, Murhabazi %A van Biljon, Elan %A Whitenack, Daniel %A Onyefuluchi, Christopher %A Emezue, Chris Chinenye %A Dossou, Bonaventure F. P. %A Sibanda, Blessing %A Bassey, Blessing %A Olabiyi, Ayodele %A Ramkilowan, Arshath %A Öktem, Alp %A Akinfaderin, Adewale %A Bashir, Abdallah %Y Cohn, Trevor %Y He, Yulan %Y Liu, Yang %S Findings of the Association for Computational Linguistics: EMNLP 2020 %D 2020 %8 November %I Association for Computational Linguistics %C Online %F nekoto-etal-2020-participatory %X 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 at https://github.com/masakhane-io/masakhane-mt. %R 10.18653/v1/2020.findings-emnlp.195 %U https://aclanthology.org/2020.findings-emnlp.195 %U https://doi.org/10.18653/v1/2020.findings-emnlp.195 %P 2144-2160