  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.
