@inproceedings{sharma-etal-2022-findings,
title = "Findings of the {CONSTRAINT} 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes",
author = "Sharma, Shivam and
Suresh, Tharun and
Kulkarni, Atharva and
Mathur, Himanshi and
Nakov, Preslav and
Akhtar, Md. Shad and
Chakraborty, Tanmoy",
editor = "Chakraborty, Tanmoy and
Akhtar, Md. Shad and
Shu, Kai and
Bernard, H. Russell and
Liakata, Maria and
Nakov, Preslav and
Srivastava, Aseem",
booktitle = "Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.constraint-1.1",
doi = "10.18653/v1/2022.constraint-1.1",
pages = "1--11",
abstract = "We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities. The task aims to delve deeper into the domain of meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or none. The shared task attracted 105 participants, but eventually only 6 submissions were made. Most of the successful submissions relied on fine-tuning pre-trained language and multimodal models along with ensembles. The best submission achieved an F1-score of 58.67.",
}
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%0 Conference Proceedings
%T Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes
%A Sharma, Shivam
%A Suresh, Tharun
%A Kulkarni, Atharva
%A Mathur, Himanshi
%A Nakov, Preslav
%A Akhtar, Md. Shad
%A Chakraborty, Tanmoy
%Y Chakraborty, Tanmoy
%Y Akhtar, Md. Shad
%Y Shu, Kai
%Y Bernard, H. Russell
%Y Liakata, Maria
%Y Nakov, Preslav
%Y Srivastava, Aseem
%S Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F sharma-etal-2022-findings
%X We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities. The task aims to delve deeper into the domain of meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or none. The shared task attracted 105 participants, but eventually only 6 submissions were made. Most of the successful submissions relied on fine-tuning pre-trained language and multimodal models along with ensembles. The best submission achieved an F1-score of 58.67.
%R 10.18653/v1/2022.constraint-1.1
%U https://aclanthology.org/2022.constraint-1.1
%U https://doi.org/10.18653/v1/2022.constraint-1.1
%P 1-11
Markdown (Informal)
[Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes](https://aclanthology.org/2022.constraint-1.1) (Sharma et al., CONSTRAINT 2022)
ACL
- Shivam Sharma, Tharun Suresh, Atharva Kulkarni, Himanshi Mathur, Preslav Nakov, Md. Shad Akhtar, and Tanmoy Chakraborty. 2022. Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes. In Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations, pages 1–11, Dublin, Ireland. Association for Computational Linguistics.