SwaQuAD-24: QA Benchmark Dataset in Swahili
It addresses the problem of low-resource language representation for NLP researchers and developers in East Africa, but is incremental as it builds on existing benchmarks.
This paper tackles the underrepresentation of Swahili in NLP by proposing the creation of a Swahili QA benchmark dataset, aiming to support applications like machine translation and healthcare chatbots while addressing ethical considerations.
This paper proposes the creation of a Swahili Question Answering (QA) benchmark dataset, aimed at addressing the underrepresentation of Swahili in natural language processing (NLP). Drawing from established benchmarks like SQuAD, GLUE, KenSwQuAD, and KLUE, the dataset will focus on providing high-quality, annotated question-answer pairs that capture the linguistic diversity and complexity of Swahili. The dataset is designed to support a variety of applications, including machine translation, information retrieval, and social services like healthcare chatbots. Ethical considerations, such as data privacy, bias mitigation, and inclusivity, are central to the dataset development. Additionally, the paper outlines future expansion plans to include domain-specific content, multimodal integration, and broader crowdsourcing efforts. The Swahili QA dataset aims to foster technological innovation in East Africa and provide an essential resource for NLP research and applications in low-resource languages.