Bloom Library: Multimodal Datasets in 300+ Languages for a Variety of Downstream Tasks
This provides a resource for linguistically diverse research in multimodal NLP, particularly benefiting low-resource language communities, though it is incremental as it compiles existing data into a new benchmark.
The authors introduced Bloom Library, a collection of multimodal datasets covering 363 languages for tasks like language modeling and image captioning, and demonstrated its utility by training models that achieved competitive performance even for low-resource languages such as Bisu.
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.