End-to-End Emotion Semantic Parsing
Xiaotong Jiang, Zhongqing Wang, Guodong Zhou · 2024
Emotion detection is the task of automatically associating one or more emotions with a text.The emotions are experienced, targeted, and caused by different semantic constituents.Therefore, it is necessary to incorporate these semantic constituents into the process of emotion detection.In this study, we propose a new task called emotion semantic parsing which aims to parse the emotion and semantic constituents into an abstract semantic tree structure.In particular, we design an end-to-end generation model to capture the relations between emotion and all the semantic constituents, and to generate them jointly.Furthermore, we employ a task decomposition strategy to capture the semantic relation among these constituents in a more cognitive and structural way.Experimental results demonstrate the importance of the proposed task, and indicate the proposed model gives superior performance compared to other models.