# Towards Neural Machine Translation for African Languages

arXiv: https://arxiv.org/abs/1811.05467 · HTML: https://mlsanity.com/paper/1811.05467/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 7 of 7)

- [OkwuGb'e: End-to-End Speech Recognition for Fon and Igbo](https://mlsanity.com/paper/2103.07762/) (2021)
- [MMTAfrica: Multilingual Machine Translation for African Languages](https://mlsanity.com/paper/2204.04306/) (2022)
- [Low-Resource Neural Machine Translation for Southern African Languages](https://mlsanity.com/paper/2104.00366/) (2021)
- [Crowdsourced Phrase-Based Tokenization for Low-Resourced Neural Machine Translation: The Case of Fon Language](https://mlsanity.com/paper/2103.08052/) (2021)
- [The IgboAPI Dataset: Empowering Igbo Language Technologies through Multi-dialectal Enrichment](https://mlsanity.com/paper/2405.00997/) (2024)
- [Shona spaCy: A Morphological Analyzer for an Under-Resourced Bantu Language](https://mlsanity.com/paper/2511.16680/) (2025)
- [The Annotation Scarcity Paradox in Low-Resource NLP Evaluation: A Decade of Acceleration and Emerging Constraints](https://mlsanity.com/paper/2605.19066/) (2026)

## Abstract (arXiv)

Given that South African education is in crisis, strategies for improvement and sustainability of high-quality, up-to-date education must be explored. In the migration of education online, inclusion of machine translation for low-resourced local languages becomes necessary. This paper aims to spur the use of current neural machine translation (NMT) techniques for low-resourced local languages. The paper demonstrates state-of-the-art performance on English-to-Setswana translation using the Autshumato dataset. The use of the Transformer architecture beat previous techniques by 5.33 BLEU points. This demonstrates the promise of using current NMT techniques for African languages.