# HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing

arXiv: https://arxiv.org/abs/2505.14311 · HTML: https://mlsanity.com/paper/2505.14311/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 81 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023)
- [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)
- [Findings of the Second BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://mlsanity.com/paper/2412.05149/) (2024)
- [MasakhaNEWS: News Topic Classification for African languages](https://mlsanity.com/paper/2304.09972/) (2023)
- [BRIGHTER: BRIdging the Gap in Human-Annotated Textual Emotion Recognition Datasets for 28 Languages](https://mlsanity.com/paper/2502.11926/) (2025)
- [AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages](https://mlsanity.com/paper/2305.06897/) (2023)
- [InkubaLM: A small language model for low-resource African languages](https://mlsanity.com/paper/2408.17024/) (2024)
- [Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation](https://mlsanity.com/paper/2205.01133/) (2022)
- [Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection](https://mlsanity.com/paper/2301.07015/) (2023)
- [HERDPhobia: A Dataset for Hate Speech against Fulani in Nigeria](https://mlsanity.com/paper/2211.15262/) (2022)
- [HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language](https://mlsanity.com/paper/2305.17690/) (2023)
- [Quantifying the redundancy between prosody and text](https://mlsanity.com/paper/2311.17233/) (2023)
- [AfriHate: A Multilingual Collection of Hate Speech and Abusive Language Datasets for African Languages](https://mlsanity.com/paper/2501.08284/) (2025)
- [The first large scale collection of diverse Hausa language datasets](https://mlsanity.com/paper/2102.06991/) (2021)
- [Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages](https://mlsanity.com/paper/2210.10692/) (2022)
- [HausaMT v1.0: Towards English-Hausa Neural Machine Translation](https://mlsanity.com/paper/2006.05014/) (2020)
- [Investigating the Impact of Language-Adaptive Fine-Tuning on Sentiment Analysis in Hausa Language Using AfriBERTa](https://mlsanity.com/paper/2501.11023/) (2025)
- [A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews](https://mlsanity.com/paper/2405.19575/) (2024)
- [Who Wrote This? Identifying Machine vs Human-Generated Text in Hausa](https://mlsanity.com/paper/2503.13101/) (2025)

## Built on by (most cited first, 2 of 2)

- [Automatic Correction of Writing Anomalies in Hausa Texts](https://mlsanity.com/paper/2506.03820/) (2025)
- [HausaMovieReview: A Benchmark Dataset for Sentiment Analysis in Low-Resource African Language](https://mlsanity.com/paper/2509.16256/) (2025)

## Abstract (arXiv)

Hausa Natural Language Processing (NLP) has gained increasing attention in recent years, yet remains understudied as a low-resource language despite having over 120 million first-language (L1) and 80 million second-language (L2) speakers worldwide. While significant advances have been made in high-resource languages, Hausa NLP faces persistent challenges, including limited open-source datasets and inadequate model representation. This paper presents an overview of the current state of Hausa NLP, systematically examining existing resources, research contributions, and gaps across fundamental NLP tasks: text classification, machine translation, named entity recognition, speech recognition, and question answering. We introduce HausaNLP (https://catalog.hausanlp.org), a curated catalog that aggregates datasets, tools, and research works to enhance accessibility and drive further development. Furthermore, we discuss challenges in integrating Hausa into large language models (LLMs), addressing issues of suboptimal tokenization and dialectal variation. Finally, we propose strategic research directions emphasizing dataset expansion, improved language modeling approaches, and strengthened community collaboration to advance Hausa NLP. Our work provides both a foundation for accelerating Hausa NLP progress and valuable insights for broader multilingual NLP research.