论文标题
Cogmen：基于上下文化GNN的多模式情绪识别
COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
论文作者
论文摘要
情绪是人类互动的固有部分，因此，必须开发理解和认识人类情绪的AI系统。在涉及各个人的谈话中，一个人的情绪受到其他说话者的话语和他们自己的情感状态的影响。在本文中，我们提出了基于上下文化的基于图形神经网络的多模式情感识别（COGMEN）系统，该系统利用本地信息（即说话者之间的间/内部依赖性）和全局信息（上下文）。拟议的模型使用基于图形神经网络（GNN）体系结构来对话中的复杂依赖关系（本地和全局信息）。我们的模型在IEMOCAP和MOSEI数据集上给出了最新的（SOTA）结果，并详细的消融实验显示了两个级别建模信息的重要性。
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.
