ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture

Youssef Mohamed; Mohamed Abdelfattah; Shyma Alhuwaider; Feifan Li; Xiangliang Zhang; Kenneth Ward Church; Mohamed Elhoseiny

ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture

Youssef Mohamed^*, Mohamed Abdelfattah, Shyma Alhuwaider, Feifan Li, Xiangliang Zhang, Kenneth Ward Church, Mohamed Elhoseiny^*

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

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

Original language	English (US)
Pages	8770-8785
Number of pages	16
State	Published - 2022
Event	2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 - Abu Dhabi, United Arab Emirates Duration: Dec 7 2022 → Dec 11 2022

Conference

Conference	2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	12/7/22 → 12/11/22

Bibliographical note

Funding Information:
This work was supported by King Abdullah University of Science and Technology (KAUST), under Award No. BAS/1/1685-01-01.

Publisher Copyright:
© 2022 Association for Computational Linguistics.

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Information Systems

Cite this

@conference{7f934874cc2b45b09cfa86c1add911a6,

title = "ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture",

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

author = "Youssef Mohamed and Mohamed Abdelfattah and Shyma Alhuwaider and Feifan Li and Xiangliang Zhang and Church, {Kenneth Ward} and Mohamed Elhoseiny",

note = "Funding Information: This work was supported by King Abdullah University of Science and Technology (KAUST), under Award No. BAS/1/1685-01-01. Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 ; Conference date: 07-12-2022 Through 11-12-2022",

year = "2022",

language = "English (US)",

pages = "8770--8785",

}

ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture. / Mohamed, Youssef; Abdelfattah, Mohamed; Alhuwaider, Shyma et al.
2022. 8770-8785 Paper presented at 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - ArtELingo

T2 - 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

AU - Mohamed, Youssef

AU - Abdelfattah, Mohamed

AU - Alhuwaider, Shyma

AU - Li, Feifan

AU - Zhang, Xiangliang

AU - Church, Kenneth Ward

AU - Elhoseiny, Mohamed

N1 - Funding Information: This work was supported by King Abdullah University of Science and Technology (KAUST), under Award No. BAS/1/1685-01-01. Publisher Copyright: © 2022 Association for Computational Linguistics.

PY - 2022

Y1 - 2022

N2 - 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 with standard splits and baseline models. We hope our work will help ease future research on multilinguality and culturally-aware AI.

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

UR - http://www.scopus.com/inward/record.url?scp=85149439367&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85149439367

SP - 8770

EP - 8785

Y2 - 7 December 2022 through 11 December 2022

ER -

ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture

Abstract

Conference

Bibliographical note

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this