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2019 IJCNLP IJCNLP 2019

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

Abstract

AbstractEmotion 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.

🌉 Interdisciplinary Bridge — Deep Learning and Interdisciplinary and Machine Learning
🐣 Hot Topic Early Bird — conversation analysis
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

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

Topics

Machine Learning > Core Methods > Classification Deep Learning > Architectures > Graph Neural Networks Interdisciplinary > Social > Affective Computing

Keywords

emotion recognition graph convolutional network emotion classification conversational context conversation analysis speaker dependency graph neural network context propagation
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