论文标题
强大的MSA：了解模态噪声对多模式情感分析的影响
Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis
论文作者
论文摘要
改善对潜在模态噪声的模型鲁棒性，这是将多模型转化为现实世界应用的重要步骤，在研究人员中受到了越来越多的关注。对于多模式情感分析（MSA），关于多模型模型是否比单峰特征更有效，也存在争论。在对这些问题的直观插图和深入分析的强调时，我们提出了强大的MSA，这是一个交互式平台，可视化模态噪声的影响以及简单的防御方法，以帮助研究人员更好地了解他们的模型如何使用不完美的现实世界数据。
Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data.
