  Multimodal Emotion Recognition in Conversation (ERC) plays an influential
role in the field of human-computer interaction and conversational robotics
since it can motivate machines to provide empathetic services. Multimodal data
modeling is an up-and-coming research area in recent years, which is inspired
by human capability to integrate multiple senses. Several graph-based
approaches claim to capture interactive information between modalities, but the
heterogeneity of multimodal data makes these methods prohibit optimal
solutions. In this work, we introduce a multimodal fusion approach named Graph
and Attention based Two-stage Multi-source Information Fusion (GA2MIF) for
emotion detection in conversation. Our proposed method circumvents the problem
of taking heterogeneous graph as input to the model while eliminating complex
redundant connections in the construction of graph. GA2MIF focuses on
contextual modeling and cross-modal modeling through leveraging Multi-head
Directed Graph ATtention networks (MDGATs) and Multi-head Pairwise Cross-modal
ATtention networks (MPCATs), respectively. Extensive experiments on two public
datasets (i.e., IEMOCAP and MELD) demonstrate that the proposed GA2MIF has the
capacity to validly capture intra-modal long-range contextual information and
inter-modal complementary information, as well as outperforms the prevalent
State-Of-The-Art (SOTA) models by a remarkable margin.
