Emotion recognition in conversation (ERC) has received increasing attention
from researchers due to its wide range of applications. As conversation has a
natural graph structure, numerous approaches used to model ERC based on graph
convolutional networks (GCNs) have yielded significant results. However, the
aggregation approach of traditional GCNs suffers from the node information
redundancy problem, leading to node discriminant information loss.
Additionally, single-layer GCNs lack the capacity to capture long-range
contextual information from the graph. Furthermore, the majority of approaches
are based on textual modality or stitching together different modalities,
resulting in a weak ability to capture interactions between modalities. To
address these problems, we present the relational bilevel aggregation graph
convolutional network (RBA-GCN), which consists of three modules: the graph
generation module (GGM), similarity-based cluster building module (SCBM) and
bilevel aggregation module (BiAM). First, GGM constructs a novel graph to
reduce the redundancy of target node information. Then, SCBM calculates the
node similarity in the target node and its structural neighborhood, where noisy
information with low similarity is filtered out to preserve the discriminant
information of the node. Meanwhile, BiAM is a novel aggregation method that can
preserve the information of nodes during the aggregation process. This module
can construct the interaction between different modalities and capture
long-range contextual information based on similarity clusters. On both the
IEMOCAP and MELD datasets, the weighted average F1 score of RBA-GCN has a
2.17$\sim$5.21\% improvement over that of the most advanced method.

通过构建新颖的图形及聚类方法，以及利用双层聚合模型捕获多模态相互作用和长程信息，提出一种提高情绪识别在对话中性能的新方法，该方法在多个数据集上相较于最先进的方法，F1 分数有 2.17％~5.21％的提升。