ITEACNet: Inverted Teacher-studEnt seArch Conversation Network

Due to the widespread applications of conversations in human-computer interaction, Conversation Emotion Recognition (CER) has attracted increasing attention from researchers. In real-world scenarios, the emotional states of both participants in a conversation tend to maintain a relatively stable pattern within the local context, and often encountering issues with incomplete data patterns. Focusing on these two key challenges, we propose a novel framework for incomplete multimodal learning in CER, called "Inverted Teacher-studEnt seArch Conversation Network (ITEACNet)." ITEACNet comprises two novel components: the "Emotion Context Changing Encoder (ECCE)" and the "Inverted Teacher-Student framework (ITS)." ECCE considers context changes from both local and global perspectives, while the ITS allows a simple teacher model to learn complete data processing methods, enabling a complex student model to follow the performance of the teacher model using incomplete data. Furthermore, we employ a Neural Architecture Search algorithm to enhance the capabilities of student model , achieving superior model performance. Finally, to align with real-world scenarios, we introduce a novel evaluation method, testing the model's performance under different missing rate conditions without altering the model weights. We conduct experiments on three benchmark CER datasets, and the results demonstrate that our ITEACNet outperforms existing methods in incomplete multimodal CER.