Abstract
Modeling conversational context is an essential step for emotion recognition in conversations. Existing works still suffer from insufficient utilization of local context information and remote context information. This article designs a hypergraph neural network, namely HNN-ERC, to better utilize local and remote contextual information. HNN-ERC combines the recurrent neural network with the conventional hypergraph neural network to strengthen connections between utterances and make each utterance receive information from other utterances better. The proposed model has empirically achieved state-of-the-art results on three benchmark datasets, demonstrating the effectiveness and superiority of the new model.
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