Hierarchically stacked graph convolution for emotion recognition in conversation

Accurate emotion recognition can drive the robot to understand human affection intentions precisely and deliver the emotional response when communicating with a person. Recently, graph structure has been applied to explicitly capture the self and inter-dependencies of speakers in the conversa-tion. However, the performance of the method is limited by inadequate discriminative information extraction based on naive graph convolution. In this paper, we propose a novel Hierarchically Stacked Graph Convolution Framework (HSGCF), which leverages hierarchical structure to extract emotional discriminative features. The proposed HSGCF uses five graph convolution layers connected hierarchically to establish a more discriminative emotional feature extractor. More importantly, to mitigate the over-smooth problem caused by deeper networks, Transformer structures with residual connection are introduced into HSGCF. Experimental results on the IEMOCAP benchmark dataset indicate the proposed framework achieves a 4.12% improvement in accuracy and a 4.80% improvement in F1 score compared with the baseline method. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).