Habeebah Kakudi


2023

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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
Findings of the Association for Computational Linguistics: 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.