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

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

30 Aug 2019IJCNLP 2019 11arXiv:1908.11540archive 2025-07-28

Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh

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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batch_graphify SenticNet/conv-emotion/DialogueGCN/model.py official repository unverified MIT (permissive) · b5c1a82b88e2f90b · report
bert_batch_preprocessing SenticNet/conv-emotion/emotion-cause-extraction/Rank-Emotion-Cause/src/data_loader.py official repository unverified MIT (permissive) · 7b9bd5de8e365f4f · report
edge_perms SenticNet/conv-emotion/DialogueGCN/model.py official repository unverified MIT (permissive) · 2097cedad5494c1c · report
pad SenticNet/conv-emotion/DialogueGCN/model.py official repository unverified MIT (permissive) · 10f8b1f4c189a2d5 · report

Tasks

Emotion ClassificationEmotion RecognitionEmotion Recognition in ConversationGraph Neural Network

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Emotion Recognition in Conversation CPED DialogueGCN Accuracy of Sentiment 47.69 #11 of 11 Archive leaderboard report
Emotion Recognition in Conversation CPED DialogueGCN Macro-F1 of Sentiment 45.12 #11 of 11 Archive leaderboard report
Emotion Recognition in Conversation IEMOCAP DialogueGCN Weighted-F1 64.37 #48 of 59 Archive leaderboard report
Emotion Recognition in Conversation MELD DialogueGCN Accuracy 59.46 #63 of 68 Archive leaderboard report
Emotion Recognition in Conversation MELD DialogueGCN Weighted-F1 58.10 #63 of 68 Archive leaderboard report
Emotion Recognition in Conversation SEMAINE DialogueGCN MAE (Arousal) 0.161 #1 of 3 Archive leaderboard report
Emotion Recognition in Conversation SEMAINE DialogueGCN MAE (Expectancy) 0.168 #1 of 3 Archive leaderboard report
Emotion Recognition in Conversation SEMAINE DialogueGCN MAE (Power) 7.68 #1 of 3 Archive leaderboard report
Emotion Recognition in Conversation SEMAINE DialogueGCN MAE (Valence) 0.157 #1 of 3 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Graph Neural Network

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