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

Deepanway Ghosal, Navonil Majumder, Soujanya Poria, Niyati Chhaya, Alexander Gelbukh
8/30/2019
cs.CLcs.LG

Official code: declare-lab/conv-emotion (1,531 stars, MIT, PyTorch, last commit 2024-03-10).

Abstract

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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Code Implementations(1)

declare-lab/conv-emotionOfficial100%
PyTorchMIT

This repo contains implementation of different architectures for emotion recognition in conversations.

1,531341Shell, PythonOct 31, 20182 years agoMIT
conversational-agentsconversational-aidialogue-systemsemotion-analysisemotion-recognition+8 more

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Cite this paper

@article{ghosal2019dialoguegcn,
  title  = {DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation},
  author = {Deepanway Ghosal and Navonil Majumder and Soujanya Poria and Niyati Chhaya and Alexander Gelbukh},
  year   = {2019},
  eprint = {1908.11540},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1908.11540}
}

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