TITLE: BIG-C: a Multimodal Multi-Purpose Dataset for Bemba 

ABSTRACT: We present BIG-C (Bemba Image Grounded Conversations), a large multimodal
dataset for Bemba. While Bemba is the most populous language of Zambia, it
exhibits a dearth of resources which render the development of language
technologies or language processing research almost impossible. The dataset is
comprised of multi-turn dialogues between Bemba speakers based on images,
transcribed and translated into English. There are more than 92,000
utterances/sentences, amounting to more than 180 hours of audio data with
corresponding transcriptions and English translations. We also provide
baselines on speech recognition (ASR), machine translation (MT) and speech
translation (ST) tasks, and sketch out other potential future multimodal uses
of our dataset. We hope that by making the dataset available to the research
community, this work will foster research and encourage collaboration across
the language, speech, and vision communities especially for languages outside
the "traditionally" used high-resourced ones. All data and code are publicly
available: https://github.com/csikasote/bigc.
We present BIG-C (Bemba Image Grounded Conversations), a large multimodal
dataset for Bemba. While Bemba is the most populous language of Zambia, it
exhibits a dearth of resources which render the development of language
technologies or language processing research almost impossible. The dataset is
comprised of multi-turn dialogues between Bemba speakers based on images,
transcribed and translated into English. There are more than 92,000
utterances/sentences, amounting to more than 180 hours of audio data with
corresponding transcriptions and English translations. We also provide
baselines on speech recognition (ASR), machine translation (MT) and speech
translation (ST) tasks, and sketch out other potential future multimodal uses
of our dataset. We hope that by making the dataset available to the research
community, this work will foster research and encourage collaboration across
the language, speech, and vision communities especially for languages outside
the "traditionally" used high-resourced ones. All data and code are publicly
available: https://github.com/csikasote/bigc.
BIG-C: a Multimodal Multi-Purpose Dataset for Bemba
http://arxiv.org/abs/2305.17202v1