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