  It has been a hot research topic to enable machines to understand human
emotions in multimodal contexts under dialogue scenarios, which is tasked with
multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received
consistent attention in recent years, where a diverse range of methods has been
proposed for securing better task performance. Most existing works treat MM-ERC
as a standard multimodal classification problem and perform multimodal feature
disentanglement and fusion for maximizing feature utility. Yet after revisiting
the characteristic of MM-ERC, we argue that both the feature multimodality and
conversational contextualization should be properly modeled simultaneously
during the feature disentanglement and fusion steps. In this work, we target
further pushing the task performance by taking full consideration of the above
insights. On the one hand, during feature disentanglement, based on the
contrastive learning technique, we devise a Dual-level Disentanglement
Mechanism (DDM) to decouple the features into both the modality space and
utterance space. On the other hand, during the feature fusion stage, we propose
a Contribution-aware Fusion Mechanism (CFM) and a Context Refusion Mechanism
(CRM) for multimodal and context integration, respectively. They together
schedule the proper integrations of multimodal and context features.
Specifically, CFM explicitly manages the multimodal feature contributions
dynamically, while CRM flexibly coordinates the introduction of dialogue
contexts. On two public MM-ERC datasets, our system achieves new
state-of-the-art performance consistently. Further analyses demonstrate that
all our proposed mechanisms greatly facilitate the MM-ERC task by making full
use of the multimodal and context features adaptively. Note that our proposed
methods have the great potential to facilitate a broader range of other
conversational multimodal tasks.
