Abstract
This paper outlines our approach to SemEval-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes, specifically addressing subtask 1. The study focuses on model fine-tuning using language models, including BERT, GPT-2, and RoBERTa, with the experiment results demonstrating optimal performance with GPT-2. Our system submission achieved a competitive ranking of 17th out of 33 teams in subtask 1, showcasing the effectiveness of the employed methodology in the context of persuasive technique identification within meme texts.- Anthology ID:
- 2024.semeval-1.158
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1092–1096
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.158
- DOI:
- Cite (ACL):
- Mohammad Osoolian, Erfan Moosavi Monazzah, and Sauleh Eetemadi. 2024. IUSTNLPLAB at SemEval-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1092–1096, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- IUSTNLPLAB at SemEval-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes (Osoolian et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/ingestion-checklist/2024.semeval-1.158.pdf