Bridging Gaps in Natural Language Processing for Yorùbá: A Systematic Review of a Decade of Progress and Prospects

TL;DR

This systematic literature review surveys Yorùbá NLP progress from 2014 to 2024, analyzing 105 primary studies to map tasks, techniques, resources, and challenges. It shows a shift from rule-based methods to multilingual pre-training and cross-lingual transfer, alongside growing resource development (corpora and datasets) to support diverse tasks such as MT, NER, POS tagging, sentiment analysis, and speech processing. Key obstacles include tonal and diacritic dependencies, morphological complexity, and persistent data scarcity, compounded by socio-cultural shifts affecting language use online. The study provides a foundation for advancing Yorùbá NLP and broader African-language inclusion in global NLP, guiding future data collection, model development, and benchmarks.

Abstract

Natural Language Processing (NLP) is becoming a dominant subset of artificial intelligence as the need to help machines understand human language looks indispensable. Several NLP applications are ubiquitous, partly due to the myriads of datasets being churned out daily through mediums like social networking sites. However, the growing development has not been evident in most African languages due to the persisting resource limitation, among other issues. Yorùbá language, a tonal and morphologically rich African language, suffers a similar fate, resulting in limited NLP usage. To encourage further research towards improving this situation, this systematic literature review aims to comprehensively analyse studies addressing NLP development for Yorùbá, identifying challenges, resources, techniques, and applications. A well-defined search string from a structured protocol was employed to search, select, and analyse 105 primary studies between 2014 and 2024 from reputable databases. The review highlights the scarcity of annotated corpora, limited availability of pre-trained language models, and linguistic challenges like tonal complexity and diacritic dependency as significant obstacles. It also revealed the prominent techniques, including rule-based methods, among others. The findings reveal a growing body of multilingual and monolingual resources, even though the field is constrained by socio-cultural factors such as code-switching and desertion of language for digital usage. This review synthesises existing research, providing a foundation for advancing NLP for Yorùbá and in African languages generally. It aims to guide future research by identifying gaps and opportunities, thereby contributing to the broader inclusion of Yorùbá and other under-resourced African languages in global NLP advancements.

Figures (10)

• Figure 1: Yorùbá Language Family Tree
• Figure 2: The systematic literature review protocol
• Figure 3: Initial Search Results across Databases
• Figure 4: PRISMA diagram of selection process
• Figure 5: Histogram of total quality score for each study