Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection
Shankar Mahadevan, Sean Benhur, Roshan Nayak, Malliga Subramanian, Kogilavani Shanmugavadivel, Kanchana Sivanraju, Bharathi Raja Chakravarthi
Abstract
Social media is an idea created to make theworld smaller and more connected. Recently,it has become a hub of fake news and sexistmemes that target women. Social Media shouldensure proper women’s safety and equality. Filteringsuch information from social media is ofparamount importance to achieving this goal.In this paper, we describe the system developedby our team for SemEval-2022 Task 5: MultimediaAutomatic Misogyny Identification. Wepropose a multimodal training methodologythat achieves good performance on both thesubtasks, ranking 4th for Subtask A (0.718macro F1-score) and 9th for Subtask B (0.695macro F1-score) while exceeding the baselineresults by good margins.- Anthology ID:
- 2022.semeval-1.75
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 550–554
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.75
- DOI:
- 10.18653/v1/2022.semeval-1.75
- Cite (ACL):
- Shankar Mahadevan, Sean Benhur, Roshan Nayak, Malliga Subramanian, Kogilavani Shanmugavadivel, Kanchana Sivanraju, and Bharathi Raja Chakravarthi. 2022. Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 550–554, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Transformers at SemEval-2022 Task 5: A Feature Extraction based Approach for Misogynous Meme Detection (Mahadevan et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.semeval-1.75.pdf