Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation

Domaine:

natural language processing

Type de record:

paper
Créateur:
OduOlaSulHun
Hôte:avatar
The linguistic diversity across the African continent presents different challenges and opportunities for machine translation. This study explores the effects of data augmentation techniques in improving translation systems in low-resource African languages. We focus on two data augmentation techniques: sentence concatenation with back translation and switch-out, applying them across six African languages. Our experiments show significant improvements in machine translation performance, with a minimum increase of 25\% in BLEU score across all six languages. We provide a comprehensive analysis and highlight the potential of these techniques to improve machine translation systems for low-resource languages, contributing to the development of more robust translation systems for under-resourced languages. 8 pages, 3 tables. Exploratory work on Data Augmentation for African Machine Translation

Visit

arxiv.org

Tasks

machine translation

Tags

Computation and Language68T50I.7

Similaires

What is the impact of synthetic data augmentation on low-resource machine translation qualityData Augmentation for Low-Resource Neural Machine TranslationA Diverse Data Augmentation Strategy for Low-Resource Neural Machine TranslationTextual Augmentation Techniques Applied to Low Resource Machine Translation: Case of SwahiliData Augmentation for Low Resource Neural Machine Translation for Sotho-Tswana LanguagesFrom drought to distress: unpacking the mental health effects of water scarcity

What is the impact of synthetic data augmentation on low-resource machine translation quality

One important issue that affects the performance of neural machine translation is the scale of avail

Data Augmentation for Low-Resource Neural Machine Translation

The quality of a Neural Machine Translation system depends substantially on the availability of siza

A Diverse Data Augmentation Strategy for Low-Resource Neural Machine Translation

One important issue that affects the performance of neural machine translation is the scale of avail

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

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

Data Augmentation for Low Resource Neural Machine Translation for Sotho-Tswana Languages

Neural Machine Translation (NMT) models have achieved remarkable performance on translating

From drought to distress: unpacking the mental health effects of water scarcity

I provide quasi-experimental evidence of the effect of drought exposure on young adults’ experiences