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

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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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Emotion Recognition in Conversation CPED DialogueGCN Accuracy of Sentiment 47.69 # 11
Macro-F1 of Sentiment 45.12 # 5
Emotion Recognition in Conversation IEMOCAP DialogueGCN Weighted-F1 64.37 # 33
Emotion Recognition in Conversation MELD DialogueGCN Weighted-F1 58.10 # 44
Accuracy 59.46 # 10
Emotion Recognition in Conversation SEMAINE DialogueGCN MAE (Valence) 0.157 # 1
MAE (Arousal) 0.161 # 2
MAE (Expectancy) 0.168 # 2
MAE (Power) 7.68 # 1


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