# Bridging Gaps in Natural Language Processing for Yor`ub'a: A Systematic Review of a Decade of Progress and Prospects arXiv: https://arxiv.org/abs/2502.17364 ยท HTML: https://mlsanity.com/paper/2502.17364/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 126 references that are in the map. - First posted on arXiv 24 Feb 2025. ## Builds on (most cited first, 30 of 30) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018) - [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021) - [AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages](https://mlsanity.com/paper/2302.08956/) (2023) - [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022) - [Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://mlsanity.com/paper/2010.03179/) (2020) - [The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation](https://mlsanity.com/paper/2110.03036/) (2021) - [AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages](https://mlsanity.com/paper/2211.03263/) (2022) - [SERENGETI: Massively Multilingual Language Models for Africa](https://mlsanity.com/paper/2212.10785/) (2022) - [AfroLID: A Neural Language Identification Tool for African Languages](https://mlsanity.com/paper/2210.11744/) (2022) - [The Effect of Domain and Diacritics in Yor`ub'a-English Neural Machine Translation](https://mlsanity.com/paper/2103.08647/) (2021) - [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022) - [XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages](https://mlsanity.com/paper/2305.11938/) (2023) - [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019) - [BibleTTS: a large, high-fidelity, multilingual, and uniquely African speech corpus](https://mlsanity.com/paper/2207.03546/) (2022) - [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023) - [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023) - [MasakhaPOS: Part-of-Speech Tagging for Typologically Diverse African Languages](https://mlsanity.com/paper/2305.13989/) (2023) - [Cheetah: Natural Language Generation for 517 African Languages](https://mlsanity.com/paper/2401.01053/) (2024) - [NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment Classification](https://mlsanity.com/paper/2305.10971/) (2023) - [Attentive Sequence-to-Sequence Learning for Diacritic Restoration of Yor`ub'a Language Text](https://mlsanity.com/paper/1804.00832/) (2018) - [Voices Unheard: NLP Resources and Models for Yor`ub'a Regional Dialects](https://mlsanity.com/paper/2406.19564/) (2024) - [TaTa: A Multilingual Table-to-Text Dataset for African Languages](https://mlsanity.com/paper/2211.00142/) (2022) - [Efficient Convolutional Neural Networks for Diacritic Restoration](https://mlsanity.com/paper/1912.06900/) (2019) - [A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery](https://mlsanity.com/paper/2011.03115/) (2020) - [YFACC: A Yor`ub'a speech-image dataset for cross-lingual keyword localisation through visual grounding](https://mlsanity.com/paper/2210.04600/) (2022) - [varepsilon K'U : Integrating Yor`ub'a cultural greetings into machine translation](https://mlsanity.com/paper/2303.17972/) (2023) - [`Ir`oy`inSpeech: A multi-purpose Yor`ub'a Speech Corpus](https://mlsanity.com/paper/2307.16071/) (2023) - [Context Models for OOV Word Translation in Low-Resource Languages](https://mlsanity.com/paper/1801.08660/) (2018) - [Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity Recognition](https://mlsanity.com/paper/2309.05311/) (2023) ## Built on by (most cited first, 2 of 2) - [Yor-Sarc: A gold-standard dataset for sarcasm detection in a low-resource African language](https://mlsanity.com/paper/2602.18964/) (2026) - [Yo-ByT5: Efficient and High-Fidelity Diacritic Restoration for Yor`ub'a](https://mlsanity.com/paper/2610.01634/) (2026) ## Abstract (arXiv) 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 myriad 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 limitations, among other issues. Yor`ub'a 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 Yor`ub'a, 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, the 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 the desertion of language for digital usage. This review synthesises existing research, providing a foundation for advancing NLP for Yor`ub'a and in African languages generally. It aims to guide future research by identifying gaps and opportunities, thereby contributing to the broader inclusion of Yor`ub'a and other under-resourced African languages in global NLP advancements.