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DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation

arXiv 1908.11540 ↗First posted 30 Aug 2019cs.CL cs.LGcited by 184

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Abstract (arXiv)

Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.

Key facts. Cited by 184 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Aug 2019.

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Citing papers per year: 2021: 33; 2022: 27; 2023: 52; 2024: 34; 2025: 13; 2026: 25.

184 papers.

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  1. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  2. 2021Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysiscited by 130
  3. 2021MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversationcited by 99
  4. 2021Directed Acyclic Graph Network for Conversational Emotion Recognitioncited by 97
  5. 2022GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversationcited by 86
  6. 2021DialogueCRN: Contextual Reasoning Networks for Emotion Recognition in Conversationscited by 71
  7. 2022UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion Recognitioncited by 61
  8. 2021DynaEval: Unifying Turn and Dialogue Level Evaluationcited by 59
  9. 2021EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTacited by 59
  10. 2021Topic-Driven and Knowledge-Aware Transformer for Dialogue Emotion Detectioncited by 56
  11. 2022Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversationcited by 56
  12. 2022MM-DFN: Multimodal Dynamic Fusion Network for Emotion Recognition in Conversationscited by 52
38 more
  1. 2022M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Databasecited by 50
  2. 2022EmoCaps: Emotion Capsule based Model for Conversational Emotion Recognitioncited by 48
  3. 2022M2FNet: Multi-modal Fusion Network for Emotion Recognition in Conversationcited by 47
  4. 2023Deep Imbalanced Learning for Multimodal Emotion Recognition in Conversationscited by 47
  5. 2021On Representation Knowledge Distillation for Graph Neural Networkscited by 38
  6. 2023A Transformer-Based Model With Self-Distillation for Multimodal Emotion Recognition in Conversationscited by 37
  7. 2021Contrast and Generation Make BART a Good Dialogue Emotion Recognizercited by 35
  8. 2021Towards Propagation Uncertainty: Edge-enhanced Bayesian Graph Convolutional Networks for Rumor Detectioncited by 34
  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. 2023A Comprehensive Survey on Multi-modal Conversational Emotion Recognition with Deep Learningcited by 32
  12. 2022COGMEN: COntextualized GNN based Multimodal Emotion recognitioNcited by 29
  13. 2023A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognitioncited by 29
  14. 2023DialogueLLM: Context and Emotion Knowledge-Tuned Large Language Models for Emotion Recognition in Conversationscited by 29
  15. 2021Graph Based Network with Contextualized Representations of Turns in Dialoguecited by 26
  16. 2021DialoGraph: Incorporating Interpretable Strategy-Graph Networks into Negotiation Dialoguescited by 25
  17. 2021Hybrid Curriculum Learning for Emotion Recognition in Conversationcited by 25
  18. 2022CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AIcited by 25
  19. 2024MIntRec2.0: A Large-scale Benchmark Dataset for Multimodal Intent Recognition and Out-of-scope Detection in Conversationscited by 25
  20. 2023DER-GCN: Dialogue and Event Relation-Aware Graph Convolutional Neural Network for Multimodal Dialogue Emotion Recognitioncited by 23
  21. 2024Efficient Long-distance Latent Relation-aware Graph Neural Network for Multi-modal Emotion Recognition in Conversationscited by 23
  22. 2022GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion Recognitioncited by 20
  23. 2023Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversationscited by 19
  24. 2024Masked Graph Learning with Recurrent Alignment for Multimodal Emotion Recognition in Conversationcited by 19
  25. 2023UniSA: Unified Generative Framework for Sentiment Analysiscited by 17
  26. 2021DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehensioncited by 16
  27. 2023Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Modelscited by 16
  28. 2021Enhancing Speaking Styles in Conversational Text-to-Speech Synthesis with Graph-based Multi-modal Context Modelingcited by 15
  29. 2021Automatically Select Emotion for Response via Personality-affected Emotion Transitioncited by 15
  30. 2021Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformercited by 14
  31. 2023Think Twice: A Human-like Two-stage Conversational Agent for Emotional Response Generationcited by 14
  32. 2023Cluster-Level Contrastive Learning for Emotion Recognition in Conversationscited by 14
  33. 2023Joyful: Joint Modality Fusion and Graph Contrastive Learning for Multimodal Emotion Recognitioncited by 14
  34. 2021Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehensioncited by 13
  35. 2021Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehensioncited by 13
  36. 2022Neutral Utterances are Also Causes: Enhancing Conversational Causal Emotion Entailment with Social Commonsense Knowledgecited by 13
  37. 2023Revisiting Disentanglement and Fusion on Modality and Context in Conversational Multimodal Emotion Recognitioncited by 13
  38. 2024SDR-GNN: Spectral Domain Reconstruction Graph Neural Network for Incomplete Multimodal Learning in Conversational Emotion Recognitioncited by 13

50 most cited of 184 All 184 (JSON)