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This paper describes the participation of team QUST in the SemEval2023 task3. The monolingual models are first evaluated with the under-sampling of themajority classes in the early stage of the task. Then, the pre-trainedmultilingual model is fine-tuned with a combination of the class weights andthe sample weights. Two different fine-tuning strategies, the task-agnostic andthe task-dependent, are further investigated. All experiments are conductedunder the 10-fold cross-validation, the multilingual approaches are superior tothe monolingual ones. The submitted system achieves the second best in Italianand Spanish (zero-shot) in subtask-1.

Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual  and Multilingual Approaches for Detecting Online News Genre, Framing and  Persuasion Techniques