SubmissionNumber#=%=#99
FinalPaperTitle#=%=#UMUTeam at SemEval-2024 Task 4: Multimodal Identification of Persuasive Techniques in Memes through Large Language Models
ShortPaperTitle#=%=#
NumberOfPages#=%=#12
CopyrightSigned#=%=#José Antonio García Díaz
JobTitle#==#
Organization#==#Universidad de Murcia, España
Abstract#==#In this manuscript we describe the UMUTeam's participation in SemEval-2024 Task 4, a shared task to identify different persuasion techniques in memes. The task is divided into three subtasks. One is a multimodal subtask of identifying whether a meme contains persuasion or not. The others are hierarchical multi-label classifications that consider textual content alone or a multimodal setting of text and visual content. This is a multilingual task, and we participated in all three subtasks but we focus only on the English dataset. Our approach is based on a fine-tuning approach with the pre-trained RoBERTa-large model. In addition, for multimodal cases with both textual and visual content, we used the LMM called LlaVa to extract image descriptions and combine them with the meme text. Our system performed well in three subtasks, achieving the tenth best result with an Hierarchical F1 of 64.774%, the fourth best in Subtask 2a with an Hierarchical F1 of 69.003%, and the eighth best in Subtask 2b with a Macro F1 of 78.660%.
Author{1}{Firstname}#=%=#ronghao
Author{1}{Lastname}#=%=#pan
Author{1}{Username}#=%=#ronghaopan
Author{1}{Email}#=%=#ronghao.pan@um.es
Author{1}{Affiliation}#=%=#Universidad de Murcia
Author{2}{Firstname}#=%=#José Antonio
Author{2}{Lastname}#=%=#García-Díaz
Author{2}{Username}#=%=#joseagd
Author{2}{Email}#=%=#joseantonio.garcia8@um.es
Author{2}{Affiliation}#=%=#Universidad de Murcia
Author{3}{Firstname}#=%=#Rafael
Author{3}{Lastname}#=%=#Valencia-García
Author{3}{Username}#=%=#valencia
Author{3}{Email}#=%=#valencia@um.es
Author{3}{Affiliation}#=%=#Universidad de Murcia

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