Chuhan Wang


2024

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CoT-based Data Augmentation Strategy for Persuasion Techniques Detection
Dailin Li | Chuhan Wang | Xin Zou | Junlong Wang | Peng Chen | Jian Wang | Liang Yang | Hongfei Lin
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)

Detecting persuasive communication is an important topic in Natural Language Processing (NLP), as it can be useful in identifying fake information on social media. We have developed a system to identify applied persuasion techniques in text fragments across four languages: English, Bulgarian, North Macedonian, and Arabic. Our system uses data augmentation methods and employs an ensemble strategy that combines the strengths of both RoBERTa and DeBERTa models. Due to limited resources, we concentrated solely on task 1, and our solution achieved the top ranking in the English track during the official assessments. We also analyse the impact of architectural decisions, data constructionand training strategies.