Disentangled Instrumentalization Learning for Dialogue Emotion Detection

Emotion detection is a pivotal research area spanning diverse fields. Despite advancements in existing methods enhancing detection accuracy and robustness through various research perspectives, the complexity of dialogue content and variability in dialogue conditions introduce significant challenges. These challenges often obscure the relationship between utterances and true emotions, leading to spurious correlations. To address these challenges, we proposeDisentangled Instrumentalization Learning (DIL), a disentangled instrumentalization learning model based on instrumental variables. Leveraging pre-extracted topic information from GPT-4 as an observable confounder, we disentangle it to learn the representation of required conditional instrumental variables. Our approach employs prior and posterior networks for covariate distribution inference, optimized via reconstruction techniques. Additionally, we introduce an instrumentalization learning method for conditional variables to effectively represent instrumental variables for emotion detection. We illustrate the variable relationships in our model using a causal Directed Acyclic Graph (DAG). Experimental results demonstrate that our model achieves a weighted-average F1-score of 69.0% with 69.4% accuracy on IEMOCAP, and a weighted-average F1-score of 67.6% on MELD, showing consistent improvements over strong baseline methods.