This paper presents the participation of team QUST in Task 8 SemEval 2024. We
first performed data augmentation and cleaning on the dataset to enhance model
training efficiency and accuracy. In the monolingual task, we evaluated
traditional deep-learning methods, multiscale positive-unlabeled framework
(MPU), fine-tuning, adapters and ensemble methods. Then, we selected the
top-performing models based on their accuracy from the monolingual models and
evaluated them in subtasks A and B. The final model construction employed a
stacking ensemble that combined fine-tuning with MPU. Our system achieved 8th
(scored 8th in terms of accuracy, officially ranked 13th) place in the official
test set in multilingual settings of subtask A. We release our system code
at:this https URL

本文介绍了 QUST 团队参与 SemEval 2024 任务 8 的研究，通过数据增强和清洗来提高模型训练效率和准确性，评估了传统的深度学习方法、多尺度正负未标记框架（MPU）、微调、适配器和集成方法，并选择了在单语任务中准确性最高的模型，在子任务 A 和 B 中进行评估。最终模型构建采用了将微调与 MPU 相结合的堆叠集成方法。我们的系统在多语言设置下的子任务 A 官方测试集中获得第八名（准确性得分第 13 名），我们在此链接发布了系统代码。