Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks
October 26, 2022 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
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Authors
Colin Leong, Joshua Nemecek, Jacob Mansdorfer, Anna Filighera, Abraham Owodunni, Daniel Whitenack
arXiv ID
2210.14712
Category
cs.CL: Computation & Language
Cross-listed
cs.AI
Citations
34
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
4 months ago
Abstract
We present Bloom Library, a linguistically diverse set of multimodal and multilingual datasets for language modeling, image captioning, visual storytelling, and speech synthesis/recognition. These datasets represent either the most, or among the most, multilingual datasets for each of the included downstream tasks. In total, the initial release of the Bloom Library datasets covers 363 languages across 32 language families. We train downstream task models for various languages represented in the data, showing the viability of the data for future work in low-resource, multimodal NLP and establishing the first known baselines for these downstream tasks in certain languages (e.g., Bisu [bzi], with an estimated population of 700 users). Some of these first-of-their-kind baselines are comparable to state-of-the-art performance for higher-resourced languages. The Bloom Library datasets are released under Creative Commons licenses on the Hugging Face datasets hub to catalyze more linguistically diverse research in the included downstream tasks.
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