HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language
- Shantipriya Parida ,
- Idris Abdulmumin ,
- Shamsuddeen Hassan Muhammad ,
- Aneesh Bose ,
- Guneet Kohli ,
- Ibrahim Said Ahmad ,
- Ketan Kotwal ,
- Sayan Deb Sarkar ,
- Ondej Bojar ,
- Habeebah Kakudi
ACL 2023 |
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.