  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.
