# Izindaba-Tindzaba: Machine learning news categorisation for Long and Short Text for isiZulu and Siswati

arXiv: https://arxiv.org/abs/2306.07426 · HTML: https://mlsanity.com/paper/2306.07426/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 12 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [Transfer Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1604.02201/) (2016)
- [Transfer Learning across Low-Resource, Related Languages for Neural Machine Translation](https://mlsanity.com/paper/1708.09803/) (2017)
- [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](https://mlsanity.com/paper/2010.12174/) (2020)
- [Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi](https://mlsanity.com/paper/2003.04986/) (2020)
- [Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary](https://mlsanity.com/paper/1705.00424/) (2017)
- [Low-Resource Language Modelling of South African Languages](https://mlsanity.com/paper/2104.00772/) (2021)
- [Low-Resource Neural Machine Translation for Southern African Languages](https://mlsanity.com/paper/2104.00366/) (2021)
- [Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages](https://mlsanity.com/paper/1810.08740/) (2018)
- [Improved Text Language Identification for the South African Languages](https://mlsanity.com/paper/1711.00247/) (2017)

## Built on by (most cited first, 2 of 2)

- [AfriMTEB and AfriE5: Benchmarking and Adapting Text Embedding Models for African Languages](https://mlsanity.com/paper/2510.23896/) (2025)
- [The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness](https://mlsanity.com/paper/2605.28190/) (2026)

## Abstract (arXiv)

Local/Native South African languages are classified as low-resource languages. As such, it is essential to build the resources for these languages so that they can benefit from advances in the field of natural language processing. In this work, the focus was to create annotated news datasets for the isiZulu and Siswati native languages based on news topic classification tasks and present the findings from these baseline classification models. Due to the shortage of data for these native South African languages, the datasets that were created were augmented and oversampled to increase data size and overcome class classification imbalance. In total, four different classification models were used namely Logistic regression, Naive bayes, XGBoost and LSTM. These models were trained on three different word embeddings namely Bag-Of-Words, TFIDF and Word2vec. The results of this study showed that XGBoost, Logistic Regression and LSTM, trained from Word2vec performed better than the other combinations.