2023/04/09 by Ye Jiang, Jiang, Ye · 2 citations
Computer Science · Social Sciences · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.2304.04190
openalex publication_date 2023/04/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.