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arXiv · 2211.13484

Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis

Huisheng Mao·Baozheng Zhang·Hua Xu·Ziqi Yuan·Yihe Liu
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Abstract

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.

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Huisheng Mao, Baozheng Zhang, Hua Xu, Ziqi Yuan, Yihe Liu. 2022-11-24. Robust-MSA: Understanding the Impact of Modality Noise on Multimodal Sentiment Analysis. https://arxiv.org/abs/2211.13484

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