Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification
Pinyi Zhang, Jingyang Chen, Junchen Shen, Zijie Zhai, Ping Li, Jie Zhang, Kai Zhang · 2024
Emotion classification has wide applications in education, robotics, virtual reality, etc.However, identifying subtle differences between fine-grained emotion categories remains challenging.Current methods typically aggregate numerous token embeddings of a sentence into a single vector, which, while being an efficient compressor, may not fully capture their complex semantic and temporal distributions.To solve this problem, we propose SEmantic ANchor Graph Neural Networks (SEAN-GNN) for fine-grained emotion classification.It learns a group of representative, multi-faceted semantic anchors in the token embedding space: using these anchors as global reference, any sentence can be projected onto them to form a "semantic-anchor graph", with node attributes and edge weights quantifying semantic and temporal information, respectively.The graph structure is well aligned across sentences and, importantly, allows for generating comprehensive emotion representations regarding K different anchors.Message passing on the anchor graph can further integrate the semantic and temporal information and refine the learned features.Empirically, SEAN-GNN produces meaningful semantic anchors and discriminative graph patterns, with promising classification results on 6 popular benchmark datasets against state-of-the-arts.