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

AbstractEmotion recognition in conversation (ERC) has attracted much attention in recent years for its necessity in widespread applications. With the development of graph neural network (GNN), recent state-of-the-art ERC models mostly use GNN to embed the intrinsic structure information of a conversation into the utterance features. In this paper, we propose a novel GNN-based model for ERC, namely S+PAGE, to better capture the speaker and position-aware conversation structure information. Specifically, we add the relative positional encoding and speaker dependency encoding in the representations of edge weights and edge types respectively to acquire a more reasonable aggregation algorithm for ERC. Besides, a two-stream conversational Transformer is presented to extract both the self and inter-speaker contextual features for each utterance. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison, whose results demonstrate the superiority of our model.