mlsanityData to 14 Oct 2026

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

arXiv 2304.04190 ↗First posted 9 Apr 2023cs.CL cs.AI cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 9 of its 15 references that are in the map. First posted on arXiv 9 Apr 2023.

Builds on

9 of 15 references.

References · 9

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Cited by year

Citing papers per year: 2024: 5; 2025: 3.

8 papers.

  1. 2024Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Textcited by 5
  2. 2024Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Sciencecited by 5
  3. 2024Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detectioncited by 3
  4. 2025Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generationcited by 3
  5. 2024Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detectioncited by 2
  6. 2024AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detectioncited by 2
  7. 2025QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrievalcited by 0
  8. 2025Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framingcited by 0