分析模因文本中的说服策略：语言模型与释义增强的融合
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment
AI总结：
本文通过微调多种语言模型并结合释义增强与均值集成，研究模因文本中说服技术的多标签检测，发现平衡训练集优于更大的不平衡训练集，并指出不当引入释义可能带来噪声。
摘要：
本文描述了我们针对模因文本中说服技术的分层多标签检测所采用的方法。我们的模型是作为近期 SemEval 任务的一部分开发的，其基础是对单个语言模型（BERT、XLM-RoBERTa 和 mBERT）进行微调，并利用基于均值的集成模型，同时通过 ChatGPT 生成释义来进行数据集增强。研究范围包括通过创新的训练技术和数据增强策略来提升模型性能。所解决的问题是如何有效识别和分类模因文本中的多种说服技术，而这一任务因该类内容的多样性和复杂性而变得困难。本文的目标是通过改进模型训练方法，并考察平衡训练数据集与不平衡训练数据集的影响，来提高检测准确率。结果与讨论中的新颖之处在于发现：使用释义进行训练能够提升模型性能，但平衡的训练集比更大的不平衡训练集更有优势。此外，分析揭示了不加区分地纳入来自不同分布的释义可能带来的潜在问题，因为这可能引入大量噪声。SemEval 2024 数据上的结果证实了这些见解，表明所提出的方法能够提高模型效能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
