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.

A Focus on Neural Machine Translation for African Languages

Domaine:

natural language processing

Type de record:

papermodeldataset
Créateur:
MarAbb
Hôte:avatar
African languages are numerous, complex and low-resourced. The datasets required for machine translation are difficult to discover, and existing research is hard to reproduce. Minimal attention has been given to machine translation for African languages so there is scant research regarding the problems that arise when using machine translation techniques. To begin addressing these problems, we trained models to translate English to five of the official South African languages (Afrikaans, isiZulu, Northern Sotho, Setswana, Xitsonga), making use of modern neural machine translation techniques. The results obtained show the promise of using neural machine translation techniques for African languages. By providing reproducible publicly-available data, code and results, this research aims to provide a starting point for other researchers in African machine translation to compare to and build upon.

Visit

arxiv.org

Tasks

machine translation

Languages

AfrikaansBirwaSetswanaTsongaZulu

Tags

Computation and LanguageMachine Learning

Similaires

Towards Neural Machine Translation for African LanguagesBenchmarking Neural Machine Translation for Southern African LanguagesANVITA-African: A Multilingual Neural Machine Translation System for African LanguagesLow-Resource Neural Machine Translation for Southern African LanguagesLow Resource Neural Machine Translation: A Benchmark for Five African LanguagesNeural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara

Towards Neural Machine Translation for African Languages

Given that South African education is in crisis, strategies for improvement and sustainability of hi

Benchmarking Neural Machine Translation for Southern African Languages

Unlike major Western languages, most African languages are very low-resourced. Furthermore, the resources that do exist are often scattered and difficult to obtain and discover. As a result, the data and code for existing research has rarely been shared, meaning re

ANVITA-African: A Multilingual Neural Machine Translation System for African Languages

This paper describes ANVITA African NMT system submitted by team ANVITA for WMT 2022 shared task on

Low-Resource Neural Machine Translation for Southern African Languages

Low-resource African languages have not fully benefited from the progress in neural machine translat

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

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

Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara

Low-resource languages present unique challenges to (neural) machine translation. We discuss the cas