Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques
This work addresses the challenge of analyzing online news content for researchers and practitioners, but it is incremental as it builds on existing methods for a specific competition task.
The paper tackled the problem of detecting online news genre, framing, and persuasion techniques by evaluating monolingual and multilingual models, finding that multilingual approaches outperformed monolingual ones in experiments, with the system achieving second best in Italian and Spanish subtasks.
This paper describes the participation of team QUST in the SemEval2023 task 3. The monolingual models are first evaluated with the under-sampling of the majority classes in the early stage of the task. Then, the pre-trained multilingual model is fine-tuned with a combination of the class weights and the sample weights. Two different fine-tuning strategies, the task-agnostic and the task-dependent, are further investigated. All experiments are conducted under the 10-fold cross-validation, the multilingual approaches are superior to the monolingual ones. The submitted system achieves the second best in Italian and Spanish (zero-shot) in subtask-1.