SwaQuAD-24: QA Benchmark Dataset in Swahili

TL;DR

This paper introduces SwaQuAD-24, a comprehensive Swahili QA benchmark dataset designed to advance NLP research and applications for the low-resource Swahili language, inspired by established benchmarks.

Contribution

It presents the creation of a high-quality, annotated Swahili QA dataset, addressing language underrepresentation and supporting diverse NLP tasks.

Findings

Dataset includes diverse, annotated question-answer pairs

Supports applications like translation and chatbots

Aims to foster NLP innovation in East Africa

Abstract

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.