  Multimodal emotion recognition in conversation (MERC) seeks to identify the
speakers' emotions expressed in each utterance, offering significant potential
across diverse fields. The challenge of MERC lies in balancing speaker modeling
and context modeling, encompassing both long-distance and short-distance
contexts, as well as addressing the complexity of multimodal information
fusion. Recent research adopts graph-based methods to model intricate
conversational relationships effectively. Nevertheless, the majority of these
methods utilize a fixed fully connected structure to link all utterances,
relying on convolution to interpret complex context. This approach can
inherently heighten the redundancy in contextual messages and excessive graph
network smoothing, particularly in the context of long-distance conversations.
To address this issue, we propose a framework that dynamically adjusts
hypergraph connections by variational hypergraph autoencoder (VHGAE), and
employs contrastive learning to mitigate uncertainty factors during the
reconstruction process. Experimental results demonstrate the effectiveness of
our proposal against the state-of-the-art methods on IEMOCAP and MELD datasets.
We release the code to support the reproducibility of this work at
https://github.com/yzjred/-HAUCL.
