  Building robust multimodal models are crucial for achieving reliable
deployment in the wild. Despite its importance, less attention has been paid to
identifying and improving the robustness of Multimodal Sentiment Analysis (MSA)
models. In this work, we hope to address that by (i) Proposing simple
diagnostic checks for modality robustness in a trained multimodal model. Using
these checks, we find MSA models to be highly sensitive to a single modality,
which creates issues in their robustness; (ii) We analyze well-known robust
training strategies to alleviate the issues. Critically, we observe that
robustness can be achieved without compromising on the original performance. We
hope our extensive study-performed across five models and two benchmark
datasets-and proposed procedures would make robustness an integral component in
MSA research. Our diagnostic checks and robust training solutions are simple to
implement and available at https://github. com/declare-lab/MSA-Robustness.

Computation and Language