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MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation

arXiv 2107.06779 ↗First posted 14 Jul 2021cs.CL cs.SD eess.AScited by 99

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Abstract (arXiv)

Emotion recognition in conversation (ERC) is a crucial component in affective dialogue systems, which helps the system understand users' emotions and generate empathetic responses. However, most works focus on modeling speaker and contextual information primarily on the textual modality or simply leveraging multimodal information through feature concatenation. In order to explore a more effective way of utilizing both multimodal and long-distance contextual information, we propose a new model based on multimodal fused graph convolutional network, MMGCN, in this work. MMGCN can not only make use of multimodal dependencies effectively, but also leverage speaker information to model inter-speaker and intra-speaker dependency. We evaluate our proposed model on two public benchmark datasets, IEMOCAP and MELD, and the results prove the effectiveness of MMGCN, which outperforms other SOTA methods by a significant margin under the multimodal conversation setting.

Key facts. Cited by 99 papers in the map (data through 7 Oct 2026). Builds on 17 of its 26 references that are in the map. First posted on arXiv 14 Jul 2021.

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17 of 26 references.

References · 17

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  3. 2016Densely Connected Convolutional Networkscited by 6,691
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  8. 2019Multimodal Transformer for Unaligned Multimodal Language Sequencescited by 637
  9. 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 636
  10. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  11. 2018DialogueRNN: An Attentive RNN for Emotion Detection in Conversationscited by 216
  12. 2019DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversationcited by 184
5 more
  1. 2018Memory Fusion Network for Multi-view Sequential Learningcited by 167
  2. 2016Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distributioncited by 158
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 12; 2023: 14; 2024: 25; 2025: 16; 2026: 29.

99 papers.

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  1. 2022GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversationcited by 86
  2. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  3. 2022UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognitioncited by 61
  4. 2022MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversationscited by 52
  5. 2022M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Databasecited by 50
  6. 2022EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognitioncited by 48
  7. 2023Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversationscited by 47
  8. 2023A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversationscited by 37
  9. 2024Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusioncited by 33
  10. 2024Revisiting Multimodal Emotion Recognition in Conversation from the Perspective of Graph Spectrumcited by 33
  11. 2021Multimodal Emotion-Cause Pair Extraction in Conversationscited by 30
  12. 2023A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognitioncited by 29
38 more
  1. 2024MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversationscited by 25
  2. 2022GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognitioncited by 20
  3. 2023Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversationscited by 19
  4. 2024Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversationcited by 19
  5. 2023UniSA: Unified Generative Framework for Sentiment Analysiscited by 17
  6. 2021Dyadformer: A Multi-modal Transformer for Long-Range Modeling of Dyadic Interactionscited by 16
  7. 2024Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Surveycited by 16
  8. 2023CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognitioncited by 14
  9. 2023Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognitioncited by 13
  10. 2024SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognitioncited by 13
  11. 2022Deep Emotion Recognition in Textual Conversations: A Surveycited by 12
  12. 2023Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognitioncited by 12
  13. 2024A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learningcited by 12
  14. 2024TelME: Teacher-leading Multimodal Fusion Network for Emotion Recognition in Conversationcited by 12
  15. 2024Recent Advances in Multimodal Affective Computing: An NLP Perspectivecited by 12
  16. 2025Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospectscited by 10
  17. 2023A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysiscited by 9
  18. 2024EmoDynamiX: Emotional Support Dialogue Strategy Prediction by Modelling MiXed Emotions and Discourse Dynamicscited by 9
  19. 2025MSE-Adapter: A Lightweight Plugin Endowing LLMs with the Capability to Perform Multimodal Sentiment Analysis and Emotion Recognitioncited by 9
  20. 2024UniMEEC: Towards Unified Multimodal Emotion Recognition and Emotion Causecited by 8
  21. 2024Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognitioncited by 8
  22. 2024Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challengescited by 7
  23. 2023Multimodal Prompt Transformer with Hybrid Contrastive Learning for Emotion Recognition in Conversationcited by 6
  24. 2024Sentiment-enhanced Graph-based Sarcasm Explanation in Dialoguecited by 6
  25. 2025Akan Cinematic Emotions (ACE): A Multimodal Multi-party Dataset for Emotion Recognition in Movie Dialoguescited by 6
  26. 2023RBA-GCN: Relational Bilevel Aggregation Graph Convolutional Network for Emotion Recognitioncited by 5
  27. 2024Curriculum Learning Meets Directed Acyclic Graph for Multimodal Emotion Recognitioncited by 5
  28. 2022Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversationcited by 4
  29. 2022A Critical Reflection and Forward Perspective on Empathy and Natural Language Processingcited by 4
  30. 2024End-to-end Semantic-centric Video-based Multimodal Affective Computingcited by 4
  31. 2025Unimodal-driven Distillation in Multimodal Emotion Recognition with Dynamic Fusioncited by 4
  32. 2022Whose Emotion Matters? Speaking Activity Localisation without Prior Knowledgecited by 3
  33. 2024Emotion-Anchored Contrastive Learning Framework for Emotion Recognition in Conversationcited by 3
  34. 2024Mamba-Enhanced Text-Audio-Video Alignment Network for Emotion Recognition in Conversationscited by 3
  35. 2024Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversationcited by 3
  36. 2024EmoVerse: Exploring Multimodal Large Language Models for Sentiment and Emotion Understandingcited by 3
  37. 2026Navigating the Emotion Tree: Hierarchical Hyperbolic RAG for Multimodal Emotion Recognitioncited by 3
  38. 2023ECQED: Emotion-Cause Quadruple Extraction in Dialogscited by 2

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