ΑΙhub.org
 

Natural language processing model for African languages


by
17 November 2021



share this:

africa
Researchers have developed an AI model to help computers work more efficiently with a wider variety of languages.

African languages have received relatively little attention from computer scientists, so few natural language processing capabilities have been available to large swaths of the continent. A new language model, developed by researchers at the University of Waterloo’s David R. Cheriton School of Computer Science, begins to fill that gap by enabling computers to analyze text in African languages for many useful tasks.

The new neural network model, which the researchers have dubbed AfriBERTa, uses deep-learning techniques to achieve state-of-the-art results for low-resource languages.

The neural network language model works specifically with 11 African languages, such as Amharic, Hausa, and Swahili, spoken collectively by more than 400 million people. It achieves competitive output quality despite learning from just one gigabyte of text, while other models require thousands of times more data.

“Pretrained language models have transformed the way computers process and analyze textual data for tasks ranging from machine translation to question answering,” said Kelechi Ogueji, a master’s student in computer science at Waterloo. “Sadly, African languages have received little attention from the research community.”

“One of the challenges is that neural networks are bewilderingly text- and computer-intensive to build. And unlike English, which has enormous quantities of available text, most of the 7,000 or so languages spoken worldwide can be characterized as low-resource, in that there is a lack of data available to feed data-hungry neural networks.”

Most of these models work using a technique known as pretraining. To accomplish this, the researcher presents the model with text where some of the words have been covered up or masked. The model then has to guess the masked words. By repeating this process, many billions of times, the model learns the statistical associations between words.

“Being able to pretrain models that are just as accurate for certain downstream tasks, but using vastly smaller amounts of data has many advantages,” said Jimmy Lin, the Cheriton Chair in Computer Science and Ogueji’s advisor. “Needing less data to train the language model means that less computation is required and consequently lower carbon emissions associated with operating massive data centres. Smaller datasets also make data curation more practical, which is one approach to reduce the biases present in the models.”

“This work takes a small but important step to bringing natural language processing capabilities to more than 1.3 billion people on the African continent.”

Assisting Ogueji and Lin in this research is Yuxin Zhu, who recently completed an undergraduate degree in computer science at Waterloo. Together, they presented their research paper, Small data? No problem! Exploring the viability of pretrained multilingual language models for low-resource languages, at the Multilingual Representation Learning Workshop at the 2021 Conference on Empirical Methods in Natural Language Processing.



tags: ,


University of Waterloo




            AIhub is supported by:



Related posts :



#IJCAI2025 distinguished paper: Combining MORL with restraining bolts to learn normative behaviour

and   04 Sep 2025
The authors introduce a framework for guiding reinforcement learning agents to comply with social, legal, and ethical norms.

How the internet and its bots are sabotaging scientific research

  03 Sep 2025
What most people have failed to fully realise is that internet research has brought along risks of data corruption or impersonation.

#ICML2025 outstanding position paper: Interview with Jaeho Kim on addressing the problems with conference reviewing

  02 Sep 2025
Jaeho argues that the AI conference peer review crisis demands author feedback and reviewer rewards.

Forthcoming machine learning and AI seminars: September 2025 edition

  01 Sep 2025
A list of free-to-attend AI-related seminars that are scheduled to take place between 2 September and 31 October 2025.
monthly digest

AIhub monthly digest: August 2025 – causality and generative modelling, responsible multimodal AI, and IJCAI in Montréal and Guangzhou

  29 Aug 2025
Welcome to our monthly digest, where you can catch up with AI research, events and news from the month past.

Interview with Benyamin Tabarsi: Computing education and generative AI

  28 Aug 2025
Read the latest interview in our series featuring the AAAI/SIGAI Doctoral Consortium participants.

The value of prediction in identifying the worst-off: Interview with Unai Fischer Abaigar

  27 Aug 2025
We hear from the winner of an outstanding paper award at ICML2025.

#IJCAI2025 social media round-up: part two

  26 Aug 2025
Find out what the participants got up to during the main part of the conference.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence