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 (this https URL), 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.
Abstract (translated)
豪萨语自然语言处理（NLP）近年来引起了越来越多的关注，然而作为一种低资源语言，尽管全球拥有超过1.2亿母语使用者和8000万第二语言使用者，其研究仍然相对较少。虽然在高资源语言领域取得了显著进展，但豪萨语NLP仍面临着诸如开放数据集有限、模型表示不足等持续挑战。本文概述了当前豪萨语NLP的状态，系统地考察了现有资源、研究成果和跨基础NLP任务（如文本分类、机器翻译、命名实体识别、语音识别及问答）的差距。我们推出了HausaNLP（此链接），这是一个经过精心策划的目录，汇总了数据集、工具和研究工作，以提高可访问性并推动进一步的发展。此外，我们讨论了在大型语言模型中整合豪萨语所面临的挑战，并解决了次优标记化及方言变化等问题。最后，我们提出了战略性的研究方向，强调数据集扩展、改进的语言建模方法以及加强社区合作对于推进豪萨语NLP的重要性。我们的工作为加速豪萨语NLP的进步提供了基础，并且为更广泛的多语言NLP研究提供了宝贵的见解。 翻译总结： 本文重点介绍了豪萨语自然语言处理（NLP）领域目前的发展状态，探讨了该领域的挑战、现有资源及未来的研究方向。通过系统地分析和提出改进策略，希望能够促进豪萨语NLP的进一步发展，并为更大范围内的多语言研究提供参考。
URL
https://arxiv.org/abs/2505.14311
