Emotion recognition in conversation (ERC) is a crucial component in affective
dialogue systems, which helps the system understand users' emotions and
generate empathetic responses. However, most works focus on modeling speaker
and contextual information primarily on the textual modality or simply
leveraging multimodal information through feature concatenation. In order to
explore a more effective way of utilizing both multimodal and long-distance
contextual information, we propose a new model based on multimodal fused graph
convolutional network, MMGCN, in this work. MMGCN can not only make use of
multimodal dependencies effectively, but also leverage speaker information to
model inter-speaker and intra-speaker dependency. We evaluate our proposed
model on two public benchmark datasets, IEMOCAP and MELD, and the results prove
the effectiveness of MMGCN, which outperforms other SOTA methods by a
significant margin under the multimodal conversation setting.

研究提出了一种基于多模态融合图卷积网络的情感识别模型，该模型能够更有效地利用多模态和长距离语境信息，并利用说话人信息对说话人之间和说话人内部依存关系进行建模，实验结果表明该模型在多模态交互下表现优异。