DialoguePCN: Perception and Cognition Network for Emotion Recognition in Conversations

Abstract

In the Emotion Recognition in Conversations (ERC) task, extracting emotional cues from the context is an effective strategy for improving model performance. However, current research has two evident limitations: firstly, irrelevant context information severely affects the extraction of emotional features at the utterance level. Secondly, in dialogues, subsequent utterances' retrieval of emotional cues does not benefit from extracted emotional cues from preceding utterances. This paper designs a Dialogue Perception Cognition Network (DialoguePCN) model, which aims to solve the issues above by simulating the perception and cognition phases of emotion in conversations. In the perception phase, DialoguePCN proposes an activation module based on a cosine similarity selection algorithm, providing a dynamic initial emotional state for the predicted utterance. In the cognition phase, the model introduces a new gating mechanism, marking the first attempt to use the extracted utterance emotion representation to reconstruct context information iteratively. This approach reduces the complexity of retrieving emotional cues from the context and solves the inherent cold-start challenge in ERC tasks. Using audio and text features, the accuracy of DialoguePCN reached 68.7% on the IEMOCAP dataset.