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

IJCNLP 2019 Deepanway GhosalNavonil MajumderSoujanya PoriaNiyati ChhayaAlexander 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... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Emotion Recognition in Conversation IEMOCAP DialogueGCN F1 64.18 # 2
Accuracy 65.25 # 2
Emotion Recognition in Conversation MELD DialogueGCN Weighted Macro-F1 58.10 # 5
Emotion Recognition in Conversation SEMAINE DialogueGCN MAE (Valence) 0.157 # 1
MAE (Arousal) 0.161 # 1
MAE (Expectancy) 0.168 # 1
MAE (Power) 7.68 # 1

Methods used in the Paper


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