@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.",
}