本文详细介绍了一项提交给SemEval-2026 Task 9的研究，该研究专注于英语和斯瓦希里语的多语言极化检测。研究人员采用了基于Transformer的模型，特别是RoBERTa-base和AfroXLMR-base，并结合了类别加权损失函数和阈值调整来处理不平衡数据集。他们的研究在二元极化检测、极化类型分类和表现形式识别方面取得了具有竞争力的F1宏平均分数，尽管错误分析表明在检测非人化和缺乏同情心方面存在挑战。 AI
影响 这项研究有助于开发能够理解和分类不同语言和文化背景下在线极化现象的模型。
排序理由 该集群包含一篇详细介绍在特定研究竞赛任务中提交的学术论文。
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arXiv:2604.06817v2 Announce Type: replace Abstract: We present SemEval-2026 Task 9, a shared task on online polarization detection, covering 22 languages and comprising over 110K annotated instances. Each data instance is multi-labeled with the presence of polarization, polarizat…
arXiv:2606.30857v1 Announce Type: new Abstract: This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization. We address all three subtasks: binary polarization detection, polarization type classification,…
This paper describes our submission to SemEval-2026 Task 9 on detecting multilingual, multicultural, and multievent online polarization. We address all three subtasks: binary polarization detection, polarization type classification, and manifestation identification for English an…