# Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda

arXiv: https://arxiv.org/abs/2505.02463 · HTML: https://mlsanity.com/paper/2505.02463/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 37 references that are in the map.
- First posted on arXiv 5 May 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [Building Machine Translation Systems for the Next Thousand Languages](https://mlsanity.com/paper/2205.03983/) (2022)
- [TICO-19: the Translation Initiative for Covid-19](https://mlsanity.com/paper/2007.01788/) (2020)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [Neural Machine Translation with Pivot Languages](https://mlsanity.com/paper/1611.04928/) (2016)
- [Data Augmentation for Neural Machine Translation using Generative Language Model](https://mlsanity.com/paper/2307.16833/) (2023)
- [AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation](https://mlsanity.com/paper/2205.04686/) (2022)
- [Fully Synthetic Data Improves Neural Machine Translation with Knowledge Distillation](https://mlsanity.com/paper/2012.15455/) (2020)
- [Building a Parallel Corpus and Training Translation Models Between Luganda and English](https://mlsanity.com/paper/2301.02773/) (2023)
- [Improving the Performance of Neural Machine Translation Involving Morphologically Rich Languages](https://mlsanity.com/paper/1612.02482/) (2016)
- [Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili](https://mlsanity.com/paper/2306.07414/) (2023)

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

- [The Saturation Point of Backtranslation in High Quality Low Resource English Gujarati Machine Translation](https://mlsanity.com/paper/2506.21566/) (2025)
- [AI-Driven Generation of Old English: A Framework for Low-Resource Languages](https://mlsanity.com/paper/2507.20111/) (2025)
- [Segmentation Beyond Defaults: Asymmetrical Byte Pair Encoding for Optimal Machine Translation Performance](https://mlsanity.com/paper/2511.03383/) (2025)

## Abstract (arXiv)

In this paper,we explore the application of Back translation (BT) as a semi-supervised technique to enhance Neural Machine Translation(NMT) models for the English-Luganda language pair, specifically addressing the challenges faced by low-resource languages. The purpose of our study is to demonstrate how BT can mitigate the scarcity of bilingual data by generating synthetic data from monolingual corpora. Our methodology involves developing custom NMT models using both publicly available and web-crawled data, and applying Iterative and Incremental Back translation techniques. We strategically select datasets for incremental back translation across multiple small datasets, which is a novel element of our approach. The results of our study show significant improvements, with translation performance for the English-Luganda pair exceeding previous benchmarks by more than 10 BLEU score units across all translation directions. Additionally, our evaluation incorporates comprehensive assessment metrics such as SacreBLEU, ChrF2, and TER, providing a nuanced understanding of translation quality. The conclusion drawn from our research confirms the efficacy of BT when strategically curated datasets are utilized, establishing new performance benchmarks and demonstrating the potential of BT in enhancing NMT models for low-resource languages.