# A Focus on Neural Machine Translation for African Languages

arXiv: https://arxiv.org/abs/1906.05685 · HTML: https://mlsanity.com/paper/1906.05685/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 15 of 15)

- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021)
- [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021)
- [Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora](https://mlsanity.com/paper/2303.03750/) (2023)
- [MMTAfrica: Multilingual Machine Translation for African Languages](https://mlsanity.com/paper/2204.04306/) (2022)
- [The Multilingual Divide and Its Impact on Global AI Safety](https://mlsanity.com/paper/2505.21344/) (2025)
- [Spelling Correction with Denoising Transformer](https://mlsanity.com/paper/2105.05977/) (2021)
- [Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks](https://mlsanity.com/paper/2208.12081/) (2022)
- [NLP for Ghanaian Languages](https://mlsanity.com/paper/2103.15475/) (2021)
- [Training Cross-Lingual embeddings for Setswana and Sepedi](https://mlsanity.com/paper/2111.06230/) (2021)
- [Geographical Distance Is The New Hyperparameter: A Case Study Of Finding The Optimal Pre-trained Language For English-isiZulu Machine Translation](https://mlsanity.com/paper/2205.08621/) (2022)
- [Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages](https://mlsanity.com/paper/2107.08772/) (2021)
- [Sentiment Analysis Across Multiple African Languages: A Current Benchmark](https://mlsanity.com/paper/2310.14120/) (2023)
- [Curated Datasets and Neural Models for Machine Translation of Informal Registers between Mayan and Spanish Vernaculars](https://mlsanity.com/paper/2404.07673/) (2024)
- [The Impact of Data Corruption on Named Entity Recognition for Low-resourced Languages](https://mlsanity.com/paper/2208.04568/) (2022)

## Abstract (arXiv)

African languages are numerous, complex and low-resourced. The datasets required for machine translation are difficult to discover, and existing research is hard to reproduce. Minimal attention has been given to machine translation for African languages so there is scant research regarding the problems that arise when using machine translation techniques. To begin addressing these problems, we trained models to translate English to five of the official South African languages (Afrikaans, isiZulu, Northern Sotho, Setswana, Xitsonga), making use of modern neural machine translation techniques. The results obtained show the promise of using neural machine translation techniques for African languages. By providing reproducible publicly-available data, code and results, this research aims to provide a starting point for other researchers in African machine translation to compare to and build upon.