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arXiv · 2112.12389

S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation

Chen Liang·Chong Yang·Jing Xu·Juyang Huang·Yongliang Wang·Yang Dong
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Abstract

Emotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. Existing ERC methods mostly model the self and inter-speaker context separately, posing a major issue for lacking enough interaction between them. In this paper, we propose a novel Speaker and Position-Aware Graph neural network model for ERC (S+PAGE), which contains three stages to combine the benefits of both Transformer and relational graph convolution network (R-GCN) for better contextual modeling. Firstly, a two-stream conversational Transformer is presented to extract the coarse self and inter-speaker contextual features for each utterance. Then, a speaker and position-aware conversation graph is constructed, and we propose an enhanced R-GCN model, called PAG, to refine the coarse features guided by a relative positional encoding. Finally, both of the features from the former two stages are input into a conditional random field layer to model the emotion transfer.

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Chen Liang, Chong Yang, Jing Xu, Juyang Huang, Yongliang Wang, Yang Dong. 2021-12-23. S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation. https://arxiv.org/abs/2112.12389

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