  Multimodal machine learning is an emerging area of research, which has
received a great deal of scholarly attention in recent years. Up to now, there
are few studies on multimodal Emotion Recognition in Conversation (ERC). Since
Graph Neural Networks (GNNs) possess the powerful capacity of relational
modeling, they have an inherent advantage in the field of multimodal learning.
GNNs leverage the graph constructed from multimodal data to perform intra- and
inter-modal information interaction, which effectively facilitates the
integration and complementation of multimodal data. In this work, we propose a
novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion
recognition in conversation. Multimodal data can be modeled as a graph, where
each data object is regarded as a node, and both intra- and inter-modal
dependencies existing between data objects can be regarded as edges. GraphMFT
utilizes multiple improved graph attention networks to capture intra-modal
contextual information and inter-modal complementary information. In addition,
the proposed GraphMFT attempts to address the challenges of existing
graph-based multimodal conversational emotion recognition models such as MMGCN.
Empirical results on two public multimodal datasets reveal that our model
outperforms the State-Of-The-Art (SOTA) approaches with the accuracy of 67.90%
and 61.30%.
