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.Our code is available at https://github.com/luftmenscher/RBA-GCN and our article with the same name has been published in IEEE/ACM Transactions on Audio,Speech,and Language Processing,vol.31,2023

翻译：对话情感识别（ERC）因其广泛的应用前景受到研究人员日益关注。由于对话具有天然的图结构，基于图卷积网络（GCN）的ERC建模方法已取得显著成果。然而，传统GCN的聚合方式存在节点信息冗余问题，导致节点判别性信息丢失。此外，单层GCN难以捕获图中的长程上下文信息。同时，现有方法多基于单一文本模态或不同模态的简单拼接，导致模态间交互捕获能力较弱。针对这些问题，我们提出关系双层聚合图卷积网络（RBA-GCN），该网络包含三个模块：图生成模块（GGM）、基于相似性的聚类构建模块（SCBM）和双层聚合模块（BiAM）。首先，GGM构建新型图结构以降低目标节点信息冗余。其次，SCBM计算目标节点及其结构邻域内的节点相似性，通过过滤低相似性噪声信息来保留节点判别性信息。同时，BiAM作为一种新型聚合方法，可在聚合过程中保留节点信息。该模块能基于相似性聚类构建不同模态间的交互，并捕获长程上下文信息。在IEMOCAP和MELD数据集上，RBA-GCN的加权平均F1分数相比最先进方法提升了2.17%~5.21%。我们的代码已开源至https://github.com/luftmenscher/RBA-GCN，同名论文已发表于IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol.31, 2023。