LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining

Samyak Agrawal, Radhika Mamidi

Abstract
In current times, memes have become one of the most popular mediums to share jokes and information with the masses over the internet. Memes can also be used as tools to spread hatred and target women through degrading content disguised as humour. The task, Multimedia Automatic Misogyny Identification (MAMI), is to detect misogyny in these memes. This task is further divided into two sub-tasks: (A) Misogynous meme identification, where a meme should be categorized either as misogynous or not misogynous and (B) Categorizing these misogynous memes into potential overlapping subcategories. In this paper, we propose models leveraging task-specific pretraining with transfer learning on Visual Linguistic models. Our best performing models scored 0.686 and 0.691 on sub-tasks A and B respectively.
Anthology ID:
2022.semeval-1.79
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
575–580
Language:
URL:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.semeval-1.79/
DOI:
10.18653/v1/2022.semeval-1.79
Bibkey:
Cite (ACL):
Samyak Agrawal and Radhika Mamidi. 2022. LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 575–580, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
LastResort at SemEval-2022 Task 5: Towards Misogyny Identification using Visual Linguistic Model Ensembles And Task-Specific Pretraining (Agrawal & Mamidi, SemEval 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/build-pipeline-with-new-library/2022.semeval-1.79.pdf
Data
Hateful Memes
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