A Label Co-occurrence Transformation Network for Joint Empathy Detection and Empathy Intent Classification

Empathy detection (ED) aims to understand the user’s empathy direction, while empathy intent classification (EIC) focuses on identifying the empathy intent behind the user’s utterance. Both tasks have garnered significant attention. Recent studies have shown that jointly training these tasks can improve model performance, as their correlation enhances the diversity of information. However, previous studies have relied solely on shallow information transfer between two task representations, failing to fully leverage the inter-task correlation, thus limiting performance. To this end, we propose a novel Label Co-occurrence Transformation Network (LCoT-Net), which models the correlation between the two tasks using the co-occurrence matrix of empathy and empathy intent labels as a medium. By performing category feature transformation at both the label and utterance levels, we achieve two-level mutual task guidance. Experimental results demonstrate that our model achieves competitive performance across various settings on two public datasets.