# HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language

Source: [HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language](<https://www.sota2.com/research/paper/68fa6f791e209131cea45d02>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-02T02:39:55.909Z

Authors: Shantipriya Parida, Idris Abdulmumin, Shamsuddeen Hassan Muhammad, Aneesh Bose, Guneet Singh Kohli, Ibrahim Said Ahmad, Ketan Kotwal, Sayan Deb Sarkar, Ond\\v{r}ej Bojar, Habeebah Adamu Kakudi

Published: 2023-05-28T10:55:31.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2305.17690>)

[PDF](<https://arxiv.org/pdf/2305.17690.pdf>)

[Implementation](<https://github.com/shantipriyap/HausaVQA/tree/main>)