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Masakhane -- Machine Translation For Africa

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arxiv 2003.11529 v1 pith:GFK5AYOP submitted 2020-03-13 cs.CL

Iroro Orife , Julia Kreutzer , Blessing Sibanda , Daniel Whitenack , Kathleen Siminyu , Laura Martinus , Jamiil Toure Ali , Jade Abbott
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Vukosi Marivate Salomon Kabongo Musie Meressa Espoir Murhabazi Orevaoghene Ahia Elan van Biljon Arshath Ramkilowan Adewale Akinfaderin Alp Öktem Wole Akin Ghollah Kioko Kevin Degila Herman Kamper Bonaventure Dossou Chris Emezue Kelechi Ogueji Abdallah Bashir
This is my paper
classification cs.CL
keywords africancommunitylanguagesafricaidentifiedlacklanguagemachine
verification ladder T0 review T1 audit T2 compute T3 formal
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Africa has over 2000 languages. Despite this, African languages account for a small portion of available resources and publications in Natural Language Processing (NLP). This is due to multiple factors, including: a lack of focus from government and funding, discoverability, a lack of community, sheer language complexity, difficulty in reproducing papers and no benchmarks to compare techniques. To begin to address the identified problems, MASAKHANE, an open-source, continent-wide, distributed, online research effort for machine translation for African languages, was founded. In this paper, we discuss our methodology for building the community and spurring research from the African continent, as well as outline the success of the community in terms of addressing the identified problems affecting African NLP.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages

    cs.CL 2025-05 conditional novelty 7.0 of 10

    NaijaVoices is a 1,838-hour, 5,455-speaker speech-text corpus for Igbo, Hausa, and Yoruba whose use in fine-tuning cuts Word Error Rates by 42-76% relative to unadapted baselines.

  2. Building a Functional Machine Translation Corpus for Kpelle

    cs.CL 2025-05 conditional novelty 6.0 of 10

    The paper introduces the first claimed public English-Kpelle parallel corpus and shows that fine-tuning NLLB on it yields BLEU up to 30 for Kpelle-to-English.

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