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
this https URL with standard splits and baseline models. We hope
our work will help ease future research on multilinguality and culturally-aware
AI.

本文介绍了 ArtELingo，这是一个旨在促进多语言和跨文化多样性的新基准和数据集。ArtELingo 添加了阿拉伯语和中文的 0.79M 注释，以及 4.8K 的西班牙语注释，以评估 “文化转移” 性能。研究表明，多样性可以提高基线模型的性能。