This paper describes our system for SemEval-2023 Task 3 Subtask 2 on Framing Detection. We used a multi-label contrastive loss for fine-tuning large pre-trained language models in a multi-lingual setting, achieving very competitive results: our system was ranked first on the official test set and on the official shared task leaderboard for five of the six languages for which we had training data and for which we could perform fine-tuning. Here, we describe our experimental setup, as well as various ablation studies. The code of our system is available at https://github.com/QishengL/SemEval2023

翻译：本文描述了我们在SemEval-2023任务3子任务2“框架检测”中的系统。我们采用多标签对比损失函数，在多语言环境下对大型预训练语言模型进行微调，取得了极具竞争力的结果：在官方测试集及官方共享任务排行榜上，针对六种拥有训练数据并可进行微调的语言中的五种，我们的系统均位列第一。本文详细阐述了实验设置及多项消融研究。系统代码已开源至https://github.com/QishengL/SemEval2023。