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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].”
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Flagged first, then by citations
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Cited by
Sorted by citations
Multimodal Transformer for Unaligned Multimodal Language Sequences
Tsai, Y.-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1656 · Cited by 2,259
“Classical crossmodal alignment, on the other hand, can be expressed as a special (step di-Table (Liang et al., 2018) 38.3 78.4 78.0 0.922 0.681 MFM (Tsai et al., 2019) 36.2 78.1 78.1 0.951 0.662 RAVEN (Wang et al., 2019) 33.2 78.0 76.6 0.915 0.691 MCTN (Pham et al., 2019) 35 (Zadeh et al., 2018b)…”
BackgroundMethodInfluentialSelf-citationMISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
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“ver representations of utterances, another line of work takes a fine-grained view to perform fusion at the word level.Approachesincludemultimodal-awarewordembeddings[56], recurrent multi-stage fusion [24], graph-based fusion [30, 60], recurrent networks (RNNs), attention-models, memory mechanisms, and transformer-base…”
MethodGandhi, A., Adhvaryu, K., Poria, S., Cambria, E., & Hussain, A. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.025 · Cited by 783
Integrating Multimodal Information in Large Pretrained Transformers
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Self-citationWords Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors
Wang, Y., Shen, Y., Liu, Z., Liang, P. P., Zadeh, A., & Morency, L. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017216 · Cited by 501
Self-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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