Summary of Analyzing Persuasive Strategies in Meme Texts: a Fusion Of Language Models with Paraphrase Enrichment, by Kota Shamanth Ramanath Nayak and Leila Kosseim
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment
by Kota Shamanth Ramanath Nayak, Leila Kosseim
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an approach to detecting persuasion techniques in meme texts using hierarchical multi-label detection. The authors fine-tune individual language models (BERT, XLM-RoBERTa, and mBERT) and leverage a mean-based ensemble model, incorporating dataset augmentation through paraphrase generation from ChatGPT. The goal is to enhance model performance through innovative training techniques and data augmentation strategies. The paper explores the impact of balanced versus unbalanced training datasets on detection accuracy, finding that training with paraphrases enhances model performance, but a balanced training set proves more advantageous than a larger unbalanced one. The analysis also reveals potential pitfalls in indiscriminately incorporating paraphrases from diverse distributions, which can introduce noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a way to find persuasion techniques in memes. It uses special language models and adds new data by changing the words around. This helps the model get better at finding the right answers. The authors also look at what happens when they use different amounts of training data and how it affects the results. They found that using paraphrases helps, but only if you’re careful not to add too much noise. |
Keywords
» Artificial intelligence » Bert » Data augmentation » Ensemble model
