The purpose of emotion recognition in conversation (ERC) is to identify the
emotion category of an utterance based on contextual information. Previous ERC
methods relied on simple connections for cross-modal fusion and ignored the
information differences between modalities, resulting in the model being unable
to focus on modality-specific emotional information. At the same time, the
shared information between modalities was not processed to generate emotions.
Information redundancy problem. To overcome these limitations, we propose a
cross-modal fusion emotion prediction network based on vector connections. The
network mainly includes two stages: the multi-modal feature fusion stage based
on connection vectors and the emotion classification stage based on fused
features. Furthermore, we design a supervised inter-class contrastive learning
module based on emotion labels. Experimental results confirm the effectiveness
of the proposed method, demonstrating excellent performance on the IEMOCAP and
MELD datasets.

基于向量连接的跨模态融合情感预测网络，包括多模态特征融合阶段和基于融合特征的情感分类阶段，同时设计了基于情感标签的监督式跨类对比学习模块，实验证实了该方法的有效性，并在 IEMOCAP 和 MELD 数据集上展现出优异的性能。