用混合模型检测22种语言的网络极化，提升跨文化内容识别准确率。
YEZE at SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization via Heterogeneous Ensembling
- 融合XLM-RoBERTa和mDeBERTa构建异构集成模型
- 独立建模+类别加权使极化检测效果更优
- 适用于多语言、多文化、多事件场景的舆情分析
本文介绍我们在SemEval-2026任务9中的系统：检测多语言、多文化及多事件在线极化。该系统在22种语言中完成三项子任务：二元极化检测、目标分类与表现形式识别。提出一种由多语言预训练模型组成的异构集成方法，结合XLM-RoBERTa-large与mDeBERTa-v3-base。研究了多任务学习、基于翻译的数据增强及类别加权等技术，以应对严重标签不平衡问题。实验表明，独立任务建模配合类别加权策略更具有效性。
原文摘要 · Abstract (English)
This paper presents our system for SemEval-2026 Task 9: Detecting Multilingual, Multicultural and Multievent Online Polarization, which identifies polarized social media content in 22 languages through three subtasks: binary detection, target classification, and manifestation identification. We propose a heterogeneous ensemble of multilingual pretrained models, combining XLM-RoBERTa-large and mDeBERTa-v3-base. We investigate techniques such as multi-task learning, translation-based data augmentation, and class weighting to improve classification performance under severe label imbalance. Our findings indicate that independent task modeling combined with class weighting is more effective.
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