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    <abstract>Multimodal Sentiment Analysis aims to predict the sentiment of video content. Recent research suggests that multimodal sentiment analysis critically depends on learning a good representation of multimodal information, which should contain both modality-invariant representations that are consistent across modalities as well as modality-specific representations. In this paper, we propose ConFEDE, a unified learning framework that jointly performs contrastive representation learning and contrastive feature decomposition to enhance the representation of multimodal information. It decomposes each of the three modalities of a video sample, including text, video frames, and audio, into a similarity feature and a dissimilarity feature, which are learned by a contrastive relation centered around the text. We conducted extensive experiments on CH-SIMS, MOSI and MOSEI to evaluate various state-of-the-art multimodal sentiment analysis methods. Experimental results show that ConFEDE outperforms all baselines on these datasets on a range of metrics.</abstract>
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%0 Conference Proceedings
%T ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis
%A Yang, Jiuding
%A Yu, Yakun
%A Niu, Di
%A Guo, Weidong
%A Xu, Yu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yang-etal-2023-confede
%X Multimodal Sentiment Analysis aims to predict the sentiment of video content. Recent research suggests that multimodal sentiment analysis critically depends on learning a good representation of multimodal information, which should contain both modality-invariant representations that are consistent across modalities as well as modality-specific representations. In this paper, we propose ConFEDE, a unified learning framework that jointly performs contrastive representation learning and contrastive feature decomposition to enhance the representation of multimodal information. It decomposes each of the three modalities of a video sample, including text, video frames, and audio, into a similarity feature and a dissimilarity feature, which are learned by a contrastive relation centered around the text. We conducted extensive experiments on CH-SIMS, MOSI and MOSEI to evaluate various state-of-the-art multimodal sentiment analysis methods. Experimental results show that ConFEDE outperforms all baselines on these datasets on a range of metrics.
%R 10.18653/v1/2023.acl-long.421
%U https://aclanthology.org/2023.acl-long.421
%U https://doi.org/10.18653/v1/2023.acl-long.421
%P 7617-7630

Markdown (Informal)

[ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis](https://aclanthology.org/2023.acl-long.421) (Yang et al., ACL 2023)

• ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis (Yang et al., ACL 2023)

ACL

• Jiuding Yang, Yakun Yu, Di Niu, Weidong Guo, and Yu Xu. 2023. ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7617–7630, Toronto, Canada. Association for Computational Linguistics.