  This paper presents HaVQA, the first multimodal dataset for visual
question-answering (VQA) tasks in the Hausa language. The dataset was created
by manually translating 6,022 English question-answer pairs, which are
associated with 1,555 unique images from the Visual Genome dataset. As a
result, the dataset provides 12,044 gold standard English-Hausa parallel
sentences that were translated in a fashion that guarantees their semantic
match with the corresponding visual information. We conducted several baseline
experiments on the dataset, including visual question answering, visual
question elicitation, text-only and multimodal machine translation.
