Conversation Clique-Based Model for Emotion Recognition In Conversation

Effective extraction and integration of valuable contextual information is the core of models for the Emotion Recognition in Conversation (ERC) task. However, a significant amount of irrelevant information is inevitably introduced when integrating long-range contextual information, perplexing the model greatly and resulting in incorrect emotion identification. To this end, we proposed a Conversation Clique-based Model (CCM), designed to extract the most efficacious contextual information to bolster the semantic quality of utterances. Specifically, we devise an utterance spatial relationship module (SpaRel) to explicitly model structural-level correlations among utterances by using GAT, and an emotion temporal relationship module (TemRel) to implicitly capture the emotion sequence constraints by employing HMM. We conduct extensive experiments on the publicly available MELD dataset, and the experimental results indicate the effectiveness of our proposed model, achieving new state-of-the-art results.