MarsEclipse 参与 SemEval-2023 任务3：基于对比学习的多语言多标签框架检测
MarsEclipse at SemEval-2023 Task 3: Multi-Lingual and Multi-Label Framing Detection with Contrastive Learning
AI总结：
本研究针对SemEval-2023任务3子任务2的多语言多标签框架检测问题，采用多标签对比损失微调预训练语言模型，在6种有训练数据的语言中5种取得官方排行榜第一的成绩，并公开了系统代码。
摘要：
本文介绍了我们针对 SemEval-2023 任务3子任务2（框架检测）构建的系统。我们采用多标签对比损失，在多语言场景下微调大型预训练语言模型，取得了极具竞争力的结果：在有训练数据且可开展微调的6种语言中，我们的系统在官方测试集及官方共享任务排行榜上有5种语言位列第一。文中还介绍了我们的实验设置以及各类消融实验。我们的系统代码可在 https://github.com/QishengL/SemEval2023 获取。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
