# Low Resource Neural Machine Translation: A Benchmark for Five African Languages

arXiv: https://arxiv.org/abs/2003.14402 · HTML: https://mlsanity.com/paper/2003.14402/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

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

- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation](https://mlsanity.com/paper/2104.08200/) (2021)
- [Benchmarking Multimodal AutoML for Tabular Data with Text Fields](https://mlsanity.com/paper/2111.02705/) (2021)
- [Natural Language Processing in Ethiopian Languages: Current State, Challenges, and Opportunities](https://mlsanity.com/paper/2303.14406/) (2023)
- [Congolese Swahili Machine Translation for Humanitarian Response](https://mlsanity.com/paper/2103.10734/) (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)
- [Cross-lingual transfer of multilingual models on low resource African Languages](https://mlsanity.com/paper/2409.10965/) (2024)
- [Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages](https://mlsanity.com/paper/2107.08772/) (2021)
- [A Tulu Resource for Machine Translation](https://mlsanity.com/paper/2403.19142/) (2024)
- [Machine Translation for Ge'ez Language](https://mlsanity.com/paper/2311.14530/) (2023)
- [Bangla-Bayanno: A 52K-Pair Bengali Visual Question Answering Dataset with LLM-Assisted Translation Refinement](https://mlsanity.com/paper/2508.19887/) (2025)
- [Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili](https://mlsanity.com/paper/2306.07414/) (2023)
- [EthioMT: Parallel Corpus for Low-resource Ethiopian Languages](https://mlsanity.com/paper/2403.19365/) (2024)
- [A Case Against Implicit Standards: Homophone Normalization in Machine Translation for Languages that use the Ge'ez Script](https://mlsanity.com/paper/2507.15142/) (2025)
- [Lutma: a Frame-Making Tool for Collaborative FrameNet Development](https://mlsanity.com/paper/2205.11840/) (2022)

## Abstract (arXiv)

Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali [SATOS]). We collected the available resources on the SATOS languages to evaluate the current state of NMT for LRLs. Our evaluation, comparing a baseline single language pair NMT model against semi-supervised learning, transfer learning, and multilingual modeling, shows significant performance improvements both in the En-LRL and LRL-En directions. In terms of averaged BLEU score, the multilingual approach shows the largest gains, up to +5 points, in six out of ten translation directions. To demonstrate the generalization capability of each model, we also report results on multi-domain test sets. We release the standardized experimental data and the test sets for future works addressing the challenges of NMT in under-resourced settings, in particular for the SATOS languages.