# AI4D -- African Language Program

arXiv: https://arxiv.org/abs/2104.02516 · HTML: https://mlsanity.com/paper/2104.02516/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [Masakhane -- Machine Translation For Africa](https://mlsanity.com/paper/2003.11529/) (2020)
- [Improving Yor`ub'a Diacritic Restoration](https://mlsanity.com/paper/2003.10564/) (2020)
- [AI4D -- African Language Dataset Challenge](https://mlsanity.com/paper/2007.11865/) (2020)
- [Neural Machine Translation for South Africa's Official Languages](https://mlsanity.com/paper/2005.06609/) (2020)

## Built on by (most cited first, 18 of 18)

- [Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning](https://mlsanity.com/paper/2402.06619/) (2024)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](https://mlsanity.com/paper/2205.12654/) (2022)
- [GlotLID: Language Identification for Low-Resource Languages](https://mlsanity.com/paper/2310.16248/) (2023)
- [AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR](https://mlsanity.com/paper/2310.00274/) (2023)
- [MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages](https://mlsanity.com/paper/2305.13989/) (2023)
- [GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages](https://mlsanity.com/paper/2410.23825/) (2024)
- [Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks](https://mlsanity.com/paper/2208.12081/) (2022)
- [AfriNames: Most ASR models "butcher" African Names](https://mlsanity.com/paper/2306.00253/) (2023)
- [State of NLP in Kenya: A Survey](https://mlsanity.com/paper/2410.09948/) (2024)
- [A Systematic Review of NLP for Ghanaian Languages: Datasets, Models, and a Research Roadmap](https://mlsanity.com/paper/2405.06818/) (2024)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [Voice of a Continent: Mapping Africa's Speech Technology Frontier](https://mlsanity.com/paper/2505.18436/) (2025)
- [Detection of Somali-written Fake News and Toxic Messages on the Social Media Using Transformer-based Language Models](https://mlsanity.com/paper/2503.18117/) (2025)
- [Redefining technology for indigenous languages](https://mlsanity.com/paper/2504.01522/) (2025)
- [The African Languages Lab: A Collaborative Approach to Advancing Low-Resource African NLP](https://mlsanity.com/paper/2510.05644/) (2025)
- [The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints](https://mlsanity.com/paper/2605.19066/) (2026)
- [A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development](https://mlsanity.com/paper/2605.22828/) (2026)

## Abstract (arXiv)

Advances in speech and language technologies enable tools such as voice-search, text-to-speech, speech recognition and machine translation. These are however only available for high resource languages like English, French or Chinese. Without foundational digital resources for African languages, which are considered low-resource in the digital context, these advanced tools remain out of reach. This work details the AI4D - African Language Program, a 3-part project that 1) incentivised the crowd-sourcing, collection and curation of language datasets through an online quantitative and qualitative challenge, 2) supported research fellows for a period of 3-4 months to create datasets annotated for NLP tasks, and 3) hosted competitive Machine Learning challenges on the basis of these datasets. Key outcomes of the work so far include 1) the creation of 9+ open source, African language datasets annotated for a variety of ML tasks, and 2) the creation of baseline models for these datasets through hosting of competitive ML challenges.