Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment

TL;DR

This paper presents a hierarchical multi-label approach using fine-tuned language models and paraphrase augmentation to detect persuasion techniques in meme texts, showing that balanced datasets and paraphrasing improve accuracy.

Contribution

It introduces a novel combination of ensemble models and paraphrase generation for better persuasion technique detection in memes, emphasizing dataset balance.

Findings

Paraphrase augmentation improves model performance.

Balanced datasets outperform unbalanced ones.

Excessive paraphrasing can introduce noise.

Abstract

This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts. Our model, developed as a part of the recent SemEval task, is based on fine-tuning individual language models (BERT, XLM-RoBERTa, and mBERT) and leveraging a mean-based ensemble model in addition to dataset augmentation through paraphrase generation from ChatGPT. The scope of the study encompasses enhancing model performance through innovative training techniques and data augmentation strategies. The problem addressed is the effective identification and classification of multiple persuasive techniques in meme texts, a task complicated by the diversity and complexity of such content. The objective of the paper is to improve detection accuracy by refining model training methods and examining the impact of balanced versus unbalanced training datasets. Novelty in the results and discussion lies in the finding that training with paraphrases enhances model performance, yet a balanced training set proves more advantageous than a larger unbalanced one. Additionally, the analysis reveals the potential pitfalls of indiscriminate incorporation of paraphrases from diverse distributions, which can introduce substantial noise. Results with the SemEval 2024 data confirm these insights, demonstrating improved model efficacy with the proposed methods.