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Multimodal Language Analysis with Recurrent Multistage Fusion
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Computational modeling of human multimodal language is an emerging research area in natural language processing spanning the language, visual and acoustic modalities.Comprehending multimodal language requires modeling not only the interactions within each modality (intra-modal interactions) but more importantly the interactions between modalities (cross-modal interactions).In this paper, we propose the Recurrent Multistage Fusion Network (RMFN) which decomposes the fusion problem into multiple stages, each of them focused on a subset of multimodal signals for specialized, effective fusion.Crossmodal interactions are modeled using this multistage fusion approach which builds upon intermediate representations of previous stages.Temporal and intra-modal interactions are modeled by integrating our proposed fusion approach with a system of recurrent neural networks.The RMFN displays state-of-the-art performance in modeling human multimodal language across three public datasets relating to multimodal sentiment analysis, emotion recognition, and speaker traits recognition.We provide visualizations to show that each stage of fusion focuses on a different subset of multimodal signals, learning increasingly discriminative multimodal representations.
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“CMU-MOSEI is the largest dataset of sentence-level sentiment analysis and emotion recognition in real-world online videos [102, 183].”
From MultiBench: Multiscale Benchmarks for Multimodal Representation Learning (2021) · cites this paper as [102]
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Liang, P. P., Lim, Y. C., Tsai, Y.-H. H., Salakhutdinov, R., & Morency, L. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1267 · Cited by 30
Self-citationMultistage linguistic conditioning of convolutional layers for speech emotion recognition
Triantafyllopoulos, A., Reichel, U. D., Liu, S., Huber, S., Eyben, F., & Schuller, B. W. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1072479 · Cited by 29
Mai, S., Hu, H., & Xing, S. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3082398 · Cited by 26
GLoMo: Global-Local Modal Fusion for Multimodal Sentiment Analysis
Yan, Z., Zhang, Y., Hu, Z., Zhang, X., Deng, J., & Ren, F. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681527 · Cited by 33
Zeng, Y., Mai, S., & Hu, H. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.109 · Cited by 31
A Multimodal Sentiment Analysis Approach Based on a Joint Chained Interactive Attention Mechanism
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A Optimized BERT for Multimodal Sentiment Analysis
Wu, J., Zhu, T., Zhu, J., Li, T., & Wang, C. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3566126 · Cited by 29
Analyzing Unaligned Multimodal Sequence via Graph Convolution and Graph Pooling FusionPreprint
Mai, S., Xing, S., He, J., Zeng, Y., & Hu, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.13572 · Cited by 26
Dynamic Invariant-Specific Representation Fusion Network for Multimodal Sentiment Analysis
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Incorporating End-to-End Speech Recognition Models for Sentiment Analysis
Lakomkin, E., Zamani, M. A., Weber, C., Magg, S., & Wermter, S. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8794468 · Cited by 23
Cross-modal dynamic convolution for multi-modal emotion recognition
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MultiBench: Multiscale Benchmarks for Multimodal Representation Learning
Liang, P. P., Lyu, Y., Fan, X., Wu, Z., Cheng, Y., Wu, J., Chen, L., Wu, P., Lee, M. A., Zhu, Y., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.07502 · Cited by 85
“…speech recognition [48] to the recent explosion of interest in language, vision, and video understanding [48] for applications such as multimedia [102, 116], affective computing [101, 127], robotics [84, 91], finance [70], dialogue [126], human-computer interaction [47, 117], and healthcare [51,…”
BackgroundSelf-citation
Metadata
- Authors
- Liang, P. P., Liu, Z., Bagher Zadeh, A., & Morency, L.-P.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 150-161
- OpenAlex
- W2886193235
- Cited by
- 230
- References
- 69
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 43 · 2023: 37 · 2022: 39 · 2021: 31 · 2020: 32 · 2019: 15 · 2018: 2
- FWCI
- 6.03
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation
- Samsung
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- cross-modal interaction, recurrent neural networks, multimodal sentiment analysis, emotion recognition
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