TITLE: Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation 

ABSTRACT: 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.
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.
Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation
http://arxiv.org/abs/2205.01133v2