  The task of multi-modal emotion recognition in conversation (MERC) aims to
analyze the genuine emotional state of each utterance based on the multi-modal
information in the conversation, which is crucial for conversation
understanding. Existing methods focus on using graph neural networks (GNN) to
model conversational relationships and capture contextual latent semantic
relationships. However, due to the complexity of GNN, existing methods cannot
efficiently capture the potential dependencies between long-distance
utterances, which limits the performance of MERC. In this paper, we propose an
Efficient Long-distance Latent Relation-aware Graph Neural Network (ELR-GNN)
for multi-modal emotion recognition in conversations. Specifically, we first
use pre-extracted text, video and audio features as input to Bi-LSTM to capture
contextual semantic information and obtain low-level utterance features. Then,
we use low-level utterance features to construct a conversational emotion
interaction graph. To efficiently capture the potential dependencies between
long-distance utterances, we use the dilated generalized forward push algorithm
to precompute the emotional propagation between global utterances and design an
emotional relation-aware operator to capture the potential semantic
associations between different utterances. Furthermore, we combine early fusion
and adaptive late fusion mechanisms to fuse latent dependency information
between speaker relationship information and context. Finally, we obtain
high-level discourse features and feed them into MLP for emotion prediction.
Extensive experimental results show that ELR-GNN achieves state-of-the-art
performance on the benchmark datasets IEMOCAP and MELD, with running times
reduced by 52\% and 35\%, respectively.
