ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis
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.- Anthology ID:
- 2023.acl-long.421
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7617–7630
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.421
- DOI:
- 10.18653/v1/2023.acl-long.421
- Cite (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.
- Cite (Informal):
- ConFEDE: Contrastive Feature Decomposition for Multimodal Sentiment Analysis (Yang et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2023.acl-long.421.pdf