# Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili

arXiv: https://arxiv.org/abs/2306.07414 · HTML: https://mlsanity.com/paper/2306.07414/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 28 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [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)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [Choosing Transfer Languages for Cross-Lingual Learning](https://mlsanity.com/paper/1905.12688/) (2019)
- [Generalized Data Augmentation for Low-Resource Translation](https://mlsanity.com/paper/1906.03785/) (2019)
- [Joey NMT: A Minimalist NMT Toolkit for Novices](https://mlsanity.com/paper/1907.12484/) (2019)
- [Improving short text classification through global augmentation methods](https://mlsanity.com/paper/1907.03752/) (2019)
- [Low Resource Neural Machine Translation: A Benchmark for Five African Languages](https://mlsanity.com/paper/2003.14402/) (2020)

## Built on by (most cited first, 1 of 1)

- [Data Augmentation With Back translation for Low Resource languages: A case of English and Luganda](https://mlsanity.com/paper/2505.02463/) (2025)

## Abstract (arXiv)

In this work we investigate the impact of applying textual data augmentation tasks to low resource machine translation. There has been recent interest in investigating approaches for training systems for languages with limited resources and one popular approach is the use of data augmentation techniques. Data augmentation aims to increase the quantity of data that is available to train the system. In machine translation, majority of the language pairs around the world are considered low resource because they have little parallel data available and the quality of neural machine translation (NMT) systems depend a lot on the availability of sizable parallel corpora. We study and apply three simple data augmentation techniques popularly used in text classification tasks; synonym replacement, random insertion and contextual data augmentation and compare their performance with baseline neural machine translation for English-Swahili (En-Sw) datasets. We also present results in BLEU, ChrF and Meteor scores. Overall, the contextual data augmentation technique shows some improvements both in the EN rightarrow SW and SW rightarrow EN directions. We see that there is potential to use these methods in neural machine translation when more extensive experiments are done with diverse datasets.