Analyzing Modality Robustness in Multimodal Sentiment Analysis

Devamanyu Hazarika, Yingting Li, Bo Cheng, Shuai Zhao, Roger Zimmermann, Soujanya Poria


Abstract
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
Anthology ID:
2022.naacl-main.50
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
685–696
Language:
URL:
https://aclanthology.org/2022.naacl-main.50
DOI:
10.18653/v1/2022.naacl-main.50
Bibkey:
Cite (ACL):
Devamanyu Hazarika, Yingting Li, Bo Cheng, Shuai Zhao, Roger Zimmermann, and Soujanya Poria. 2022. Analyzing Modality Robustness in Multimodal Sentiment Analysis. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 685–696, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Analyzing Modality Robustness in Multimodal Sentiment Analysis (Hazarika et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.50.pdf
Code
 declare-lab/msa-robustness