Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation

Cong-Duy Nguyen, Thong Nguyen, Duc Vu, Anh Luu

Abstract
The effectiveness of a model is heavily reliant on the quality of the fusion representation of multiple modalities in multimodal sentiment analysis. Moreover, each modality is extracted from raw input and integrated with the rest to construct a multimodal representation. Although previous methods have proposed multimodal representations and achieved promising results, most of them focus on forming positive and negative pairs, neglecting the variation in sentiment scores within the same class. Additionally, they fail to capture the significance of unimodal representations in the fusion vector. To address these limitations, we introduce a framework called Supervised Angular-based Contrastive Learning for Multimodal Sentiment Analysis. This framework aims to enhance discrimination and generalizability of the multimodal representation and overcome biases in the fusion vector’s modality. Our experimental results, along with visualizations on two widely used datasets, demonstrate the effectiveness of our approach.
Anthology ID:
2023.findings-emnlp.980
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14714–14724
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.980
DOI:
10.18653/v1/2023.findings-emnlp.980
Bibkey:
Cite (ACL):
Cong-Duy Nguyen, Thong Nguyen, Duc Vu, and Anh Luu. 2023. Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14714–14724, Singapore. Association for Computational Linguistics.
Cite (Informal):
Improving Multimodal Sentiment Analysis: Supervised Angular margin-based Contrastive Learning for Enhanced Fusion Representation (Nguyen et al., Findings 2023)
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