We address a notable gap in Natural Language Processing (NLP) by introducing a
collection of resources designed to improve Machine Translation (MT) for low-resource …

In this paper, we investigate the use of N-gram models and Large Pre-trained Multilingual
models for Language Identification (LID) across 11 South African languages. For N-gram …

Natural language processing (NLP) has made significant progress for well-resourced
languages such as English but lagged behind for low-resource languages like Setswana …

The need for large text corpora has increased with the advent of pretrained language
models and, in particular, the discovery of scaling laws for these models. Most available …

Africa has over 2000 languages; however, those languages are not well represented in the
existing natural language processing ecosystem. African languages lack essential digital …

The digitalization of most businesses through the integration of artificial intelligence (AI)
presents a great threat to many professionals asking themselves if their skill set will still be …

There are several challenges that hinder the development of Setswana-to-English machine
translation systems. A key obstacle is the absence of machine-readable knowledge …

The Nguni languages have over 20 million home language speakers in South Africa. There
has been considerable growth in the datasets for Nguni languages, but so far no analysis of …

In the current digital age, languages lacking digital presence face an imminent risk of
extinction. In addition, the absence of digital resources poses a significant obstacle to the …

One of the major challenges of the Department of Education in South Africa is the low
numeracy skills amongst South African learners. This study seeks to spotlight the low …