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

2025-05-20

AI Summary

Hausa—a low-resource language with ~120 million L1 and ~80 million L2 speakers—faces critical bottlenecks in foundational NLP tasks (text classification, machine translation, NER, ASR, QA), including severe data scarcity, inadequate model representations, suboptimal tokenization for LLMs, and significant dialectal variation. This work systematically surveys the state of Hausa NLP and introduces HausaNLP.org, the first open, unified resource directory, curating 30+ datasets and tools. Through multi-task benchmarking and tokenizer evaluation, we identify five core resource gaps. We propose a collaborative LLM co-development methodology for low-resource languages, comprising metadata standardization, LLM adaptability diagnostics, and a community-driven development framework. Our contributions establish a reusable paradigm and technical roadmap for resource construction in Hausa and analogous low-resource languages.

Technology Category

• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• Addressing understudied Hausa NLP despite large speaker base
• Overcoming limited datasets and poor model representation
• Improving Hausa integration in LLMs and dialect handling

Innovation

• Curated catalog aggregating Hausa NLP resources
• Addressing tokenization and dialectal variation in LLMs
• Proposing dataset expansion and community collaboration