# Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara

arXiv: https://arxiv.org/abs/2011.05284 · HTML: https://mlsanity.com/paper/2011.05284/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2020.

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

- [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks](https://mlsanity.com/paper/2208.12081/) (2022)
- [Domain-specific MT for Low-resource Languages: The case of Bambara-French](https://mlsanity.com/paper/2104.00041/) (2021)
- [Bambara Language Dataset for Sentiment Analysis](https://mlsanity.com/paper/2108.02524/) (2021)
- [Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition](https://mlsanity.com/paper/2103.07186/) (2021)
- [Grammatical Error Correction for Low-Resource Languages: The Case of Zarma](https://mlsanity.com/paper/2410.15539/) (2024)
- [Ngambay-French Neural Machine Translation (sba-Fr)](https://mlsanity.com/paper/2308.13497/) (2023)
- [A comparison of pipelines for the translation of a low resource language based on transformers](https://mlsanity.com/paper/2509.12514/) (2025)

## Abstract (arXiv)

Low-resource languages present unique challenges to (neural) machine translation. We discuss the case of Bambara, a Mande language for which training data is scarce and requires significant amounts of pre-processing. More than the linguistic situation of Bambara itself, the socio-cultural context within which Bambara speakers live poses challenges for automated processing of this language. In this paper, we present the first parallel data set for machine translation of Bambara into and from English and French and the first benchmark results on machine translation to and from Bambara. We discuss challenges in working with low-resource languages and propose strategies to cope with data scarcity in low-resource machine translation (MT).