NL4IA at SemEval-2023 Task 3: A Comparison of Sequence Classification and Token Classification to Detect Persuasive Techniques

Abstract

The following system description presents our approach to the detection of persuasion techniques in online news. The given task has been framed as a multi-label classification problem. In a multi-label classification problem, each input chunk—in this case paragraph—is assigned one of several class labels. Span level annotations were also provided. In order to assign class labels to the given documents, we opted for RoBERTa (A Robustly Optimized BERT Pretraining Approach) for both approaches—sequence and token classification. Starting off with a pre-trained model for language representation, we fine-tuned this model on the given classification task with the provided annotated data in supervised training steps.