Legend at ArAIEval Shared Task: Persuasion Technique Detection using a Language-Agnostic Text Representation Model
This addresses the challenge of identifying persuasion techniques in Arabic text for NLP researchers, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled the problem of detecting persuasion techniques in Arabic tweets and news articles by fine-tuning the XLM-RoBERTa model, achieving a micro F1 score of 0.64 on a competition test set.
In this paper, we share our best performing submission to the Arabic AI Tasks Evaluation Challenge (ArAIEval) at ArabicNLP 2023. Our focus was on Task 1, which involves identifying persuasion techniques in excerpts from tweets and news articles. The persuasion technique in Arabic texts was detected using a training loop with XLM-RoBERTa, a language-agnostic text representation model. This approach proved to be potent, leveraging fine-tuning of a multilingual language model. In our evaluation of the test set, we achieved a micro F1 score of 0.64 for subtask A of the competition.