  This paper introduces ArtELingo, a new benchmark and dataset, designed to
encourage work on diversity across languages and cultures. Following ArtEmis, a
collection of 80k artworks from WikiArt with 0.45M emotion labels and
English-only captions, ArtELingo adds another 0.79M annotations in Arabic and
Chinese, plus 4.8K in Spanish to evaluate "cultural-transfer" performance. More
than 51K artworks have 5 annotations or more in 3 languages. This diversity
makes it possible to study similarities and differences across languages and
cultures. Further, we investigate captioning tasks, and find diversity improves
the performance of baseline models. ArtELingo is publicly available at
https://www.artelingo.org/ with standard splits and baseline models. We hope
our work will help ease future research on multilinguality and culturally-aware
AI.
