Emotions are an inherent part of human interactions, and consequently, it is
imperative to develop AI systems that understand and recognize human emotions.
During a conversation involving various people, a person's emotions are
influenced by the other speaker's utterances and their own emotional state over
the utterances. In this paper, we propose COntextualized Graph Neural Network
based Multimodal Emotion recognitioN (COGMEN) system that leverages local
information (i.e., inter/intra dependency between speakers) and global
information (context). The proposed model uses Graph Neural Network (GNN) based
architecture to model the complex dependencies (local and global information)
in a conversation. Our model gives state-of-the-art (SOTA) results on IEMOCAP
and MOSEI datasets, and detailed ablation experiments show the importance of
modeling information at both levels.

本文提出了一种基于上下文图神经网络的多模态情感识别系统 (COGMEN)，该模型利用本地信息和全局信息来模拟对话中复杂的依赖关系，并通过 IEMOCAP 和 MOSEI 数据集取得了最优结果。