# Benchmarking Neural Machine Translation for Southern African Languages

arXiv: https://arxiv.org/abs/1906.10511 · HTML: https://mlsanity.com/paper/1906.10511/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 9 of 9)

- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages](https://mlsanity.com/paper/2205.12654/) (2022)
- [AfroMT: Pretraining Strategies and Reproducible Benchmarks for Translation of 8 African Languages](https://mlsanity.com/paper/2109.04715/) (2021)
- [Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?](https://mlsanity.com/paper/2203.08850/) (2022)
- [BembaSpeech: A Speech Recognition Corpus for the Bemba Language](https://mlsanity.com/paper/2102.04889/) (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)
- [Training Cross-Lingual embeddings for Setswana and Sepedi](https://mlsanity.com/paper/2111.06230/) (2021)
- [Seeing the Unseen: Visual Similarity for Pixel Language Model Adaptation](https://mlsanity.com/paper/2608.30541/) (2026)

## Abstract (arXiv)

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared. This has lead a struggle to reproduce reported results, and few publicly available benchmarks for African machine translation models exist. To start to address these problems, we trained neural machine translation models for 5 Southern African languages on publicly-available datasets. Code is provided for training the models and evaluate the models on a newly released evaluation set, with the aim of spur future research in the field for Southern African languages.