提出三路径架构搜索模型，提升多模态假新闻检测的融合与泛化能力。
Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection
- 设计动态双路径+静态路径的可搜索架构，灵活应对缺失模态场景。
- 在微博和Twitter数据集上相比基线提升1.8%~3.2%准确率。
- 适合处理部分模态缺失的假新闻检测任务，尤其适用于社交平台应用。
多模态假新闻检测已成为社交媒体平台的关键问题。尽管现有方法已取得先进性能，但仍面临两大挑战：(1) 模型架构固化导致多模态信息融合效果不佳；(2) 在仅含部分模态的假新闻上泛化能力弱。为此，我们提出一种新型且灵活的三路径增强神经架构搜索模型MUSE。MUSE包含两条动态路径以检测部分模态假新闻，以及一条静态路径用于挖掘潜在的多模态相关性。实验结果表明，MUSE在多个基准上均实现稳定性能提升。
原文摘要 · Abstract (English)
Multimodal fake news detection has become one of the most crucial issues on social media platforms. Although existing methods have achieved advanced performance, two main challenges persist: (1) Under-performed multimodal news information fusion due to model architecture solidification, and (2) weak generalization ability on partial-modality contained fake news. To meet these challenges, we propose a novel and flexible triple path enhanced neural architecture search model MUSE. MUSE includes two dynamic paths for detecting partial-modality contained fake news and a static path for exploiting potential multimodal correlations. Experimental results show that MUSE achieves stable performance improvement over the baselines.
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