@inproceedings{bakhshande-naderi-2024-cvcoders,
title = "{CV}coders on {S}emeval-2024 Task 4",
author = "Bakhshande, Fatemezahra and
Naderi, Mahdieh",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.267",
pages = "1912--1918",
abstract = "In this paper, we present our methodology for addressing the SemEval 2024 Task 4 on {``}Multilingual Detection of Persuasion Techniques in Memes.{''} Our method focuses on identifying persuasion techniques within textual and multimodal meme content using a combination of preprocessing techniques and established models. By integrating advanced preprocessing methods, such as the OpenAI API for text processing, and utilizing a multimodal architecture combining VGG for image feature extraction and GPT-2 for text feature extraction, we achieve improved model performance. To handle class imbalance, we employ Focal Loss as the loss function and AdamW as the optimizer. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance in the task. Notably, our system attains an F1 macro score of 0.67 and an F1 micro score of 0.74 on the test dataset, ranking third among all participants in the competition. Our findings highlight the importance of robust preprocessing techniques and model selection in effectively analyzing memes for persuasion techniques, contributing to efforts to combat misinformation on social media platforms.",
}
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<abstract>In this paper, we present our methodology for addressing the SemEval 2024 Task 4 on “Multilingual Detection of Persuasion Techniques in Memes.” Our method focuses on identifying persuasion techniques within textual and multimodal meme content using a combination of preprocessing techniques and established models. By integrating advanced preprocessing methods, such as the OpenAI API for text processing, and utilizing a multimodal architecture combining VGG for image feature extraction and GPT-2 for text feature extraction, we achieve improved model performance. To handle class imbalance, we employ Focal Loss as the loss function and AdamW as the optimizer. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance in the task. Notably, our system attains an F1 macro score of 0.67 and an F1 micro score of 0.74 on the test dataset, ranking third among all participants in the competition. Our findings highlight the importance of robust preprocessing techniques and model selection in effectively analyzing memes for persuasion techniques, contributing to efforts to combat misinformation on social media platforms.</abstract>
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%0 Conference Proceedings
%T CVcoders on Semeval-2024 Task 4
%A Bakhshande, Fatemezahra
%A Naderi, Mahdieh
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F bakhshande-naderi-2024-cvcoders
%X In this paper, we present our methodology for addressing the SemEval 2024 Task 4 on “Multilingual Detection of Persuasion Techniques in Memes.” Our method focuses on identifying persuasion techniques within textual and multimodal meme content using a combination of preprocessing techniques and established models. By integrating advanced preprocessing methods, such as the OpenAI API for text processing, and utilizing a multimodal architecture combining VGG for image feature extraction and GPT-2 for text feature extraction, we achieve improved model performance. To handle class imbalance, we employ Focal Loss as the loss function and AdamW as the optimizer. Experimental results demonstrate the effectiveness of our approach, achieving competitive performance in the task. Notably, our system attains an F1 macro score of 0.67 and an F1 micro score of 0.74 on the test dataset, ranking third among all participants in the competition. Our findings highlight the importance of robust preprocessing techniques and model selection in effectively analyzing memes for persuasion techniques, contributing to efforts to combat misinformation on social media platforms.
%U https://aclanthology.org/2024.semeval-1.267
%P 1912-1918
Markdown (Informal)
[CVcoders on Semeval-2024 Task 4](https://aclanthology.org/2024.semeval-1.267) (Bakhshande & Naderi, SemEval 2024)
ACL
- Fatemezahra Bakhshande and Mahdieh Naderi. 2024. CVcoders on Semeval-2024 Task 4. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 1912–1918, Mexico City, Mexico. Association for Computational Linguistics.