Causal relationships between Emotions and Dialog Acts

Emotions and Dialog Acts (DAs) are phenomena in interpersonal communication that are informative of a person's underlying thoughts and feelings. Thus, affective computing and dialog system researchers are interested in understanding and detecting emotions and DAs in dialogs. Previous studies on emotion recognition and dialog act classification observed that utilizing one feature for classification of the other can improve model performance, which benefits the advancement of affective dialog systems. However, theoretical explanation of such gain remains unclear. In linguistic research, the relationship between an emotion and a DA is often investigated qualitatively. In this work, we conducted both qualitative and quantitative analyses examining the relationships between emotions and DAs. Through statistical analyses, causal discovery methods, and a crowd-sourcing study, using two datasets of emotional dialog we identified emotion-DA causal pairs that provide empirical evidences to existing linguistic theories, while supporting addressing these two features together in affective dialog systems. Moreover, our work offers an effective methodology for revealing linguistic discoveries with a data-driven approach.