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 Kakudi
Abstract
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.- Anthology ID:
- 2023.findings-acl.646
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 10162–10183
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.646
- DOI:
- 10.18653/v1/2023.findings-acl.646
- Cite (ACL):
- Shantipriya Parida, Idris Abdulmumin, Shamsuddeen Hassan Muhammad, Aneesh Bose, Guneet Singh Kohli, Ibrahim Said Ahmad, Ketan Kotwal, Sayan Deb Sarkar, Ondřej Bojar, and Habeebah Kakudi. 2023. HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language. In Findings of the Association for Computational Linguistics: ACL 2023, pages 10162–10183, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language (Parida et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.findings-acl.646.pdf