# Building a Parallel Corpus and Training Translation Models Between Luganda and English

arXiv: https://arxiv.org/abs/2301.02773 · HTML: https://mlsanity.com/paper/2301.02773/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 7 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [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)
- [Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory](https://mlsanity.com/paper/1806.08748/) (2018)
- [Towards Neural Machine Translation for Edoid Languages](https://mlsanity.com/paper/2003.10704/) (2020)
- [Luganda Text-to-Speech Machine](https://mlsanity.com/paper/2005.05447/) (2020)
- [Combining Word and Character Vector Representation on Neural Machine Translation](https://mlsanity.com/paper/2009.05935/) (2020)

## Built on by (most cited first, 3 of 3)

- [Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda](https://mlsanity.com/paper/2505.02463/) (2025)
- [Fast Training of NMT Model with Data Sorting](https://mlsanity.com/paper/2308.08153/) (2023)
- [Advancing AI with Integrity: Ethical Challenges and Solutions in Neural Machine Translation](https://mlsanity.com/paper/2404.01070/) (2024)

## Abstract (arXiv)

Neural machine translation (NMT) has achieved great successes with large datasets, so NMT is more premised on high-resource languages. This continuously underpins the low resource languages such as Luganda due to the lack of high-quality parallel corpora, so even 'Google translate' does not serve Luganda at the time of this writing. In this paper, we build a parallel corpus with 41,070 pairwise sentences for Luganda and English which is based on three different open-sourced corpora. Then, we train NMT models with hyper-parameter search on the dataset. Experiments gave us a BLEU score of 21.28 from Luganda to English and 17.47 from English to Luganda. Some translation examples show high quality of the translation. We believe that our model is the first Luganda-English NMT model. The bilingual dataset we built will be available to the public.