# Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation arXiv: https://arxiv.org/abs/2205.01133 ยท HTML: https://mlsanity.com/paper/2205.01133/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 20 of its 34 references that are in the map. - First posted on arXiv 2 May 2022. ## Builds on (most cited first, 20 of 20) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014) - [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015) - [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016) - [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021) - [Convolutional Sequence to Sequence Learning](https://mlsanity.com/paper/1705.03122/) (2017) - [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016) - [OpenNMT: Open-source Toolkit for Neural Machine Translation](https://mlsanity.com/paper/1709.03815/) (2017) - [Tensor2Tensor for Neural Machine Translation](https://mlsanity.com/paper/1803.07416/) (2018) - [Training Tips for the Transformer Model](https://mlsanity.com/paper/1804.00247/) (2018) - [Revisiting Low-Resource Neural Machine Translation: A Case Study](https://mlsanity.com/paper/1905.11901/) (2019) - [Dynamic Context-guided Capsule Network for Multimodal Machine Translation](https://mlsanity.com/paper/2009.02016/) (2020) - [Generative Imagination Elevates Machine Translation](https://mlsanity.com/paper/2009.09654/) (2020) - [Gumbel-Attention for Multi-modal Machine Translation](https://mlsanity.com/paper/2103.08862/) (2021) - [Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation](https://mlsanity.com/paper/1907.08948/) (2019) - [The first large scale collection of diverse Hausa language datasets](https://mlsanity.com/paper/2102.06991/) (2021) - [hauWE: Hausa Words Embedding for Natural Language Processing](https://mlsanity.com/paper/1911.10708/) (2019) ## Built on by (most cited first, 13 of 13) - [MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition](https://mlsanity.com/paper/2210.12391/) (2022) - [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025) - [Correcting FLORES Evaluation Dataset for Four African Languages](https://mlsanity.com/paper/2409.00626/) (2024) - [HaVQA: A Dataset for Visual Question Answering and Multimodal Research in Hausa Language](https://mlsanity.com/paper/2305.17690/) (2023) - [BIG-C: a Multimodal Multi-Purpose Dataset for Bemba](https://mlsanity.com/paper/2305.17202/) (2023) - [Chitranuvad: Adapting Multi-Lingual LLMs for Multimodal Translation](https://mlsanity.com/paper/2502.20420/) (2025) - [Large Multimodal Models for Low-Resource Languages: A Survey](https://mlsanity.com/paper/2502.05568/) (2025) - [The State of Computer Vision Research in Africa](https://mlsanity.com/paper/2401.11617/) (2024) - [HausaNLP: Current Status, Challenges and Future Directions for Hausa Natural Language Processing](https://mlsanity.com/paper/2505.14311/) (2025) - [AfriNLLB: Efficient Translation Models for African Languages](https://mlsanity.com/paper/2602.09373/) (2026) - [Brotherhood at WMT 2024: Leveraging LLM-Generated Contextual Conversations for Cross-Lingual Image Captioning](https://mlsanity.com/paper/2409.15052/) (2024) - [NaijaNLP: A Survey of Nigerian Low-Resource Languages](https://mlsanity.com/paper/2502.19784/) (2025) - [A Survey of Text and Speech Resources for Hausa and Fongbe: Availability, Quality, and Gaps for NLP Development](https://mlsanity.com/paper/2605.22828/) (2026) ## Abstract (arXiv) Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations. The visual information can serve as a valuable piece of context information to decrease the ambiguity of input sentences. Despite the increasing popularity of such a technique, good and sizeable datasets are scarce, limiting the full extent of their potential. Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It is estimated that about 100 to 150 million people speak the language, with more than 80 million indigenous speakers. This is more than any of the other Chadic languages. Despite a large number of speakers, the Hausa language is considered low-resource in natural language processing (NLP). This is due to the absence of sufficient resources to implement most NLP tasks. While some datasets exist, they are either scarce, machine-generated, or in the religious domain. Therefore, there is a need to create training and evaluation data for implementing machine learning tasks and bridging the research gap in the language. This work presents the Hausa Visual Genome (HaVG), a dataset that contains the description of an image or a section within the image in Hausa and its equivalent in English. To prepare the dataset, we started by translating the English description of the images in the Hindi Visual Genome (HVG) into Hausa automatically. Afterward, the synthetic Hausa data was carefully post-edited considering the respective images. The dataset comprises 32,923 images and their descriptions that are divided into training, development, test, and challenge test set. The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, and image description, among various other natural language processing and generation tasks.