Introduction
Africa is home to over 2,000 languages, yet most natural language processing (NLP) research has focused on high-resource languages like English, Chinese, and Spanish. This creates a significant gap in AI accessibility for hundreds of millions of African speakers.
At VE.KE, we've been working on NLP systems for African languages for over three years. In this post, we'll share the challenges we've encountered and the approaches we've developed to address them.
The Challenge of Low-Resource Languages
The primary challenge in building NLP models for African languages is data scarcity. Most machine learning models, especially transformer-based architectures, require massive amounts of training data to perform well. For languages like Swahili, Amharic, or Yoruba, this data simply doesn't exist at the scale available for English.
Consider these numbers:
This represents a difference of two orders of magnitude in available text data.
Our Approach: Transfer Learning and Data Augmentation
Multilingual Foundation Models
Rather than training models from scratch for each language, we leverage multilingual foundation models like mBERT and XLM-R as starting points. These models have seen data from 100+ languages during pre-training, giving them a baseline understanding of linguistic structure.
Targeted Data Collection
We've partnered with local organizations, universities, and media companies to collect high-quality text data in target languages. This includes:
Data Augmentation Techniques
We employ several techniques to artificially expand our training data:
Results and Impact
Our Swahili NLP model now achieves 89% accuracy on sentiment analysis tasks, compared to 72% for off-the-shelf multilingual models. More importantly, we've deployed these models in production systems that serve millions of users across East Africa.
What's Next
We're continuing to expand our coverage to more African languages and improving our models' performance. We're also working on:
If you're interested in collaborating on African language NLP research, we'd love to hear from you.