Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks

Colin Leong, Joshua Nemecek, Jacob Mansdorfer, Anna Filighera, Abraham Owodunni, Daniel Whitenack


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.
Anthology ID:
2022.emnlp-main.590
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
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Publisher:
Association for Computational Linguistics
Note:
Pages:
8608–8621
Language:
URL:
https://aclanthology.org/2022.emnlp-main.590
DOI:
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Cite (ACL):
Colin Leong, Joshua Nemecek, Jacob Mansdorfer, Anna Filighera, Abraham Owodunni, and Daniel Whitenack. 2022. Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8608–8621, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks (Leong et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.590.pdf