@inproceedings{mohamed-etal-2022-artelingo,
title = "{A}rt{EL}ingo: A Million Emotion Annotations of {W}iki{A}rt with Emphasis on Diversity over Language and Culture",
author = "Mohamed, Youssef and
Abdelfattah, Mohamed and
Alhuwaider, Shyma and
Li, Feifan and
Zhang, Xiangliang and
Church, Kenneth and
Elhoseiny, Mohamed",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.600",
doi = "10.18653/v1/2022.emnlp-main.600",
pages = "8770--8785",
abstract = "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. 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 {`}www.artelingo.org{`} with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI.",
}
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<abstract>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. 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 ‘www.artelingo.org‘ with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI.</abstract>
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%0 Conference Proceedings
%T ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture
%A Mohamed, Youssef
%A Abdelfattah, Mohamed
%A Alhuwaider, Shyma
%A Li, Feifan
%A Zhang, Xiangliang
%A Church, Kenneth
%A Elhoseiny, Mohamed
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F mohamed-etal-2022-artelingo
%X 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. 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 ‘www.artelingo.org‘ with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI.
%R 10.18653/v1/2022.emnlp-main.600
%U https://aclanthology.org/2022.emnlp-main.600
%U https://doi.org/10.18653/v1/2022.emnlp-main.600
%P 8770-8785
Markdown (Informal)
[ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture](https://aclanthology.org/2022.emnlp-main.600) (Mohamed et al., EMNLP 2022)
ACL