KenSwQuAD—A Question Answering Dataset for Swahili Low-resource Language

The need for question-answering (QA) datasets in low-resource languages is the motivation of this research, leading to development Kencorpus Swahili Question Answering Dataset (KenSwQuAD). This dataset annotated from raw story texts Swahili, a language that predominantly spoken eastern Africa and other parts world. Question-answering are important machine comprehension natural tasks such as internet search dialog systems. Machine learning systems training data gold-standard set developed research. research engaged annotators formulate QA pairs collected by project, Kenyan corpus. project 1,445 total 2,585 with at least 5 each, resulting final 7,526 pairs. A quality assurance 12.5% confirmed were all correctly annotated. proof concept on applying task can be usable tasks. KenSwQuAD has also contributed resourcing language.