IUSTNLPLAB at S em E val-2024 Task 4: Multilingual Detection of Persuasion Techniques in Memes (2024)

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.

## introduction propaganda is the term used when information is intentionally molded to promote a specific agenda. memes typically involve combining an image with text. in deceptive memes, the image serves to either enhance or complement a technique employed in the text, or it independently conveys one or more persuasive techniques. in subtask 1 of semeval-2024 task 4 @xcite , the challenge involves identifying which of the 20 persuasion techniques, organized hierarchically, are utilized, based on the textual content of a meme. for this problem, gpt2 was chosen as the base model after experiments on gpt2, bert and roberta. after that, the model was fine-tuned on the given data set and after doing error analysis and comparing them with true labels, the threshold of sensitivity was changed manually to get best results. in addition, we tried to fine-tune model on semeval-2023 task 3 dataset which is similar to the given dataset for this task. however, the results didn't improve. regarding the noticeable change in scores just by changing the threshold of predicting labels, we realized the importance of error analysis and the easy tricks comes after actually understanding the behavior of model and it's problems. we have made all the code necessary to replicate our results available in the paper's github repository. foot_0 2 background ## conclusion in this paper, we examined different models and finally by choosing gpt2, we presented a model for the problem of identifying persuasion techniques in english memes. with the help of the presented model and adjusting the threshold for this model, we were able to reach a score of 0.624 for f1-score. our work demonstrates the effect of choosing the appropriate model for training and the need to perform error analysis to improve the accuracy of the model.