# NaijaNLP: A Survey of Nigerian Low-Resource Languages

arXiv: https://arxiv.org/abs/2502.19784 · HTML: https://mlsanity.com/paper/2502.19784/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 54 of its 293 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 30 of 54)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [The State and Fate of Linguistic Diversity and Inclusion in the NLP World](https://mlsanity.com/paper/2004.09095/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020)
- [MasakhaNER: Named Entity Recognition for African Languages](https://mlsanity.com/paper/2103.11811/) (2021)
- [Adapting Pre-trained Language Models to African Languages via Multilingual Adaptive Fine-Tuning](https://mlsanity.com/paper/2204.06487/) (2022)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://mlsanity.com/paper/2010.12858/) (2020)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [Facebook AI WMT21 News Translation Task Submission](https://mlsanity.com/paper/2108.03265/) (2021)
- [Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go](https://mlsanity.com/paper/2203.08351/) (2022)
- [AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages](https://mlsanity.com/paper/2211.03263/) (2022)
- [IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models](https://mlsanity.com/paper/2406.03368/) (2024)
- [Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation](https://mlsanity.com/paper/2406.16678/) (2024)
- [AfroLID: A Neural Language Identification Tool for African Languages](https://mlsanity.com/paper/2210.11744/) (2022)
- [Trans-Tokenization and Cross-lingual Vocabulary Transfers: Language Adaptation of LLMs for Low-Resource NLP](https://mlsanity.com/paper/2408.04303/) (2024)
- [Massive vs. Curated Word Embeddings for Low-Resourced Languages. The Case of Yor`ub'a and Twi](https://mlsanity.com/paper/1912.02481/) (2019)
- [StarSpace: Embed All The Things!](https://mlsanity.com/paper/1709.03856/) (2017)
- [Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance](https://mlsanity.com/paper/2311.01108/) (2023)
- [OkwuGb'e: End-to-End Speech Recognition for Fon and Igbo](https://mlsanity.com/paper/2103.07762/) (2021)
- [Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation](https://mlsanity.com/paper/2205.01133/) (2022)
- [Correcting FLORES Evaluation Dataset for Four African Languages](https://mlsanity.com/paper/2409.00626/) (2024)

## Abstract (arXiv)

With over 500 languages in Nigeria, three languages - Hausa, Yor`ub'a and Igbo spoken by more than 175 million people, account for about 65% of the languages. However, these languages are classed as low-resource due to insufficient digital resources to support tasks in computational linguistics. While several research efforts and initiatives have been presented, a coherent understanding of the state of classic Natural Language Processing (NLP) spanning grammatical formalisation to linguistic resources that support models development is lacking. This study presents the first comprehensive review of the state of affairs in NLP research across the three major Nigerian languages (NaijaNLP). We quantitatively assess the available linguistic resources and identify key challenges. Of the 293 reviewed studies, 27.6% contributed new linguistic resources. This finding highlights a strong reliance on repurposing existing data rather than creating new resources. Also, language-specific challenges, such as morphological analysis and effective representation of diacritics, remain under-explored. To advance NaijaNLP and LR-NLP more broadly, we echo the need for more collaborative efforts in resource enrichment, comprehensive annotation, and increased community support.