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

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arxiv 1908.11540 v1 pith:GAXCHN6A submitted 2019-08-30 cs.CL cs.LG

Deepanway Ghosal , Navonil Majumder , Soujanya Poria , Niyati Chhaya , Alexander Gelbukh This is my paper · ORCID
classification cs.CLcs.LG
keywords emotiongraphnetworkdialoguegcnrecognitioncontextconversationconvolutional
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TTS-CtrlNet: Time varying emotion aligned text-to-speech generation with ControlNet

    cs.SD 2025-07 conditional novelty 7.0 of 10

    TTS-CtrlNet adds time-varying emotion control to a frozen flow-matching TTS model using a ControlNet-style trainable copy, improving emotion similarity metrics while preserving the base model's voice cloning.

  2. EmoEUS: Uncertainty Supervision for Multimodal Emotion Recognition in Conversation

    cs.MM 2026-07 conditional novelty 6.0 of 10

    Modeling each modality as a Gaussian and supervising its variance with the 2-Wasserstein distance to emotion cluster centers improves IEMOCAP/MELD accuracy by about 0.5-0.8 points over listed baselines.

  3. EII-SCL: Harnessing Emotional Inertia for Multimodal Emotion Recognition in Conversation

    cs.MM 2026-07 conditional novelty 5.0 of 10

    A plug-in contrastive loss using speaker-local 'emotional inertia' hard negatives improves multimodal emotion-recognition accuracy and F1 on IEMOCAP and MELD by about 0.5–2 points.

  4. RAMer: Reconstruction-based Adversarial Model for Multi-party Multi-modal Multi-label Emotion Recognition

    cs.CV 2025-02 conditional novelty 5.0 of 10

    RAMer achieves state-of-the-art multi-label emotion recognition on three benchmarks by combining reconstruction-based adversarial training, contrastive learning, a personality cue, and a stack shuffle augmentation to ...

  5. Sync-TVA: A Graph-Attention Framework for Multimodal Emotion Recognition with Cross-Modal Fusion

    cs.MM 2025-07 conditional novelty 4.0 of 10

    Sync-TVA reports modest accuracy and weighted-F1 improvements over prior graph-based models on MELD and IEMOCAP, using modality-specific enhancement and cross-modal graph fusion.

  6. A Survey: Learning Embodied Intelligence from Physical Simulators and World Models

    cs.RO 2025-07 conditional novelty 4.0 of 10

    Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.

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