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.

翻译：最近,由于在保健、教育和人力资源等不同领域可能广泛应用,研究人员对谈话(ERC)中的情感认识问题给予了极大关注。在本论文中,我们介绍了基于图表神经网络的神经网络方法“对话图集网络”(DilogueGCN)。我们利用对话者的自我和口语依赖来模拟对话环境,以了解情感。通过图集网络,“对话GCN”解决了目前基于RNN的方法中存在的背景传播问题。我们的经验显示,这一方法缓解了这些问题,同时优于一些基本情感分类数据集的当前最新水平。