# Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora

arXiv: https://arxiv.org/abs/2303.03750 · HTML: https://mlsanity.com/paper/2303.03750/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 13 of 13)

- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages](https://mlsanity.com/paper/2010.02353/) (2020)
- [A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation](https://mlsanity.com/paper/2205.02022/) (2022)
- [Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World](https://mlsanity.com/paper/2210.08523/) (2022)
- [Learning Joint Multilingual Sentence Representations with Neural Machine Translation](https://mlsanity.com/paper/1704.04154/) (2017)
- [Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment](https://mlsanity.com/paper/1806.06478/) (2018)
- [The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations](https://mlsanity.com/paper/1702.03964/) (2017)
- [A Corpus for Multilingual Document Classification in Eight Languages](https://mlsanity.com/paper/1805.09821/) (2018)
- [A Focus on Neural Machine Translation for African Languages](https://mlsanity.com/paper/1906.05685/) (2019)
- [Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi](https://mlsanity.com/paper/2003.04986/) (2020)
- [An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages](https://mlsanity.com/paper/2109.00165/) (2021)
- [Multilingual Named Entity Recognition Using Pretrained Embeddings, Attention Mechanism and NCRF](https://mlsanity.com/paper/1906.09978/) (2019)

## Built on by (most cited first, 12 of 12)

- [GlotLID: Language Identification for Low-Resource Languages](https://mlsanity.com/paper/2310.16248/) (2023)
- [GlotCC: An Open Broad-Coverage CommonCrawl Corpus and Pipeline for Minority Languages](https://mlsanity.com/paper/2410.23825/) (2024)
- [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://mlsanity.com/paper/2510.10159/) (2025)
- [Toucan: Many-to-Many Translation for 150 African Language Pairs](https://mlsanity.com/paper/2407.04796/) (2024)
- [PuoBERTa: Training and evaluation of a curated language model for Setswana](https://mlsanity.com/paper/2310.09141/) (2023)
- [TIGQA:An Expert Annotated Question Answering Dataset in Tigrinya](https://mlsanity.com/paper/2404.17194/) (2024)
- [OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report](https://mlsanity.com/paper/2602.13139/) (2026)
- [The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages](https://mlsanity.com/paper/2502.15916/) (2025)
- [Swivuriso: The South African Next Voices Multilingual Speech Dataset](https://mlsanity.com/paper/2512.02201/) (2025)
- [MphayaNER: Named Entity Recognition for Tshivenda](https://mlsanity.com/paper/2304.03952/) (2023)
- [Pula: Training Large Language Models for Setswana](https://mlsanity.com/paper/2408.02239/) (2024)
- [From N-grams to Pre-trained Multilingual Models For Language Identification](https://mlsanity.com/paper/2410.08728/) (2024)

## Abstract (arXiv)

This paper introduces two multilingual government themed corpora in various South African languages. The corpora were collected by gathering the South African Government newspaper (Vuk'uzenzele), as well as South African government speeches (ZA-gov-multilingual), that are translated into all 11 South African official languages. The corpora can be used for a myriad of downstream NLP tasks. The corpora were created to allow researchers to study the language used in South African government publications, with a focus on understanding how South African government officials communicate with their constituents. In this paper we highlight the process of gathering, cleaning and making available the corpora. We create parallel sentence corpora for Neural Machine Translation (NMT) tasks using Language-Agnostic Sentence Representations (LASER) embeddings. With these aligned sentences we then provide NMT benchmarks for 9 indigenous languages by fine-tuning a massively multilingual pre-trained language model.