  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.
