HaVQA: A Dataset for Visual Question Answering and Multimodal Research
  in Hausa Language
Shantipriya Parida Idris Abdulmumin Shamsuddeen Hassan Muhammad Aneesh Bose Guneet Singh Kohli Ibrahim Said Ahmad Ketan Kotwal Sayan Deb Sarkar Ondřej Bojar Habeebah Adamu Kakudi
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.