Puer at S em E val-2024 Task 4: Fine-tuning Pre-trained Language Models for Meme Persuasion Technique Detection (2024)

Abstract: The paper summarizes our research on multilingual detection of persuasion techniques in memes for the SemEval-2024 Task 4. Our work focused on English-Subtask 1, implemented based on a roberta-large pre-trained model provided by the transforms tool that was fine-tuned into a corpus of social media posts. Our method significantly outperforms the officially released baseline method, and ranked 7th in English-Subtask 1 for the test set. This paper also compares the performances of different deep learning model architectures, such as BERT, ALBERT, and XLM-RoBERTa, on multilingual detection of persuasion techniques in memes. The experimental source code covered in the paper will later be sourced from Github.

## introduction memes has been steadily increasing as human behavior as social media platforms have become more prevalent. this type of content is known for its rapid spread, achieved through the manipulation of audience psychology and the blurring of logical relationships. memes are generally made up of stacked images and text. the essence of its expression in order to generate an emotional effect is actually the skillful role of three persuasive strategies @xcite in rhetorical portions: if we further split these three categories of persuasion strategies into twenty-two, scientists are able to obtain textual and visual features from memes for analysis. for instance, it is feasible to efficiently decrease or prevent the spread of hate speech, racial discrimination, and deceptive information by analysing memes, then simultaneously preserving the peace and stability of social media. memes can assist merchants in quickly capturing market trends, allowing them to carry out advertising and marketing operations more effectively and raise brand influence. memes helps media workers in understanding the concerns of their audiences. memes in politics have the potential to help voters demonstrate their policy views. the goal of the task is to classify corpora of text in memes and assign them to relevant persuasive strategies. our work in semeval-2024 task 4 focuses on subtask 1,and this is a multi label classification task. our contrbutions can be highlighted as follows: ## related work since the introduction of bert in 2018 @xcite , its impact on the landscape of natural language processing and multimodal analysis has been profound @xcite . bert and its advanced derivatives, such as roberta @xcite , xlm-roberta @xcite , and al-bert @xcite , have demonstrated their robust capabilities in a broad spectrum of applications, ranging from sentiment analysis to complex multimodal tasks that combine textual and visual data. notably, roberta has been recognized for its superior performance in accurately classifying sentiment @xcite , emotion @xcite , and offensive content @xcite , highlighting the model's efficiency as a sophisticated text encoder. the advent of these models has revolutionized the approach to analyzing diverse datasets and tasks, enabling nuanced understanding and processing of complex language patterns. this has been particularly evident in the domain of multimodal research, where bert-based models have been instrumental in advancing the study of visual and textual data integration @xcite @xcite . the success of these models in such a unique and culturally rich context exemplifies their broad applicability and the expanding frontiers of computational linguistics and content analysis. in conclusion, the inclusion of bert and its variants in the analysis of persuasion techniques in memes marks a significant milestone in the field @xcite @xcite . it underscores the models' unparalleled flexibility and their emerging role in understanding the complexities of human communication in the digital age. as these models continue to evolve, their contribution to bridging the gap between textual and visual data analysis will undoubtedly pave the way for groundbreaking research and applications across diverse disci-plines. ## conclusion in our participation in semeval-2024 task 4, specifically english-subtask 1, we focused on addressing the challenge of multi-label text classification. our study investigated the impact of various pre-trained models on experimental outcomes and the influence of different hyperparameters on model performance. ultimately, the adoption of the roberta-large model fine-tuned on social media posts led to outstanding performance, achieving a hierarchical f1 score of 0.66 on the test set and securing a commendable 7th position among english-subtask 1 participants. in our experimentation, we did not pursue a finergrained classification within the multi-label task. moving forward, our future research direction will pivot towards fine-grained multi-label classification. this would entail optimizing the loss function or implementing multi-level classification techniques to enhance the model's generalization capabilities.