SubmissionNumber#=%=#28
FinalPaperTitle#=%=#CLaC at SemEval-2024 Task 4: Decoding Persuasion in Memes – An Ensemble of Language Models with Paraphrase Augmentation
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Kota Shamanth Ramanath Nayak
JobTitle#==#
Organization#==#Computational Linguistics @Concordia (CLaC), Concordia University, 1455 Blvd. De Maisonneuve Ouest, Montreal, Quebec H3G 1M8
Abstract#==#This paper describes our approach to SemEval-2024 Task 4 subtask 1, focusing on hierarchical multi-label detection of persuasion techniques in meme texts. Our approach was based on fine-tuning individual language models (BERT, XLM-RoBERTa, and mBERT) and leveraging a mean-based  ensemble model. Additional strategies included dataset augmentation through the TC dataset and paraphrase generation as well as the fine-tuning of individual classification thresholds for each class. During testing, our system outperformed the baseline in all languages except for Arabic, where no significant improvement was reached. Analysis of the results seem to indicate that our dataset augmentation strategy and per-class threshold fine-tuning may have introduced noise and exacerbated the dataset imbalance.
Author{1}{Firstname}#=%=#Kota Shamanth Ramanath
Author{1}{Lastname}#=%=#Nayak
Author{1}{Username}#=%=#shamanthnyk
Author{1}{Email}#=%=#nayak.shamanth2000@gmail.com
Author{1}{Affiliation}#=%=#Concordia University
Author{2}{Firstname}#=%=#Leila
Author{2}{Lastname}#=%=#Kosseim
Author{2}{Username}#=%=#kosseim
Author{2}{Email}#=%=#leila.kosseim@concordia.ca
Author{2}{Affiliation}#=%=#Concordia University

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