Jointly Identifying Rhetoric and Implicit Emotions via Multi-Task Learning

Rhetorical implicit emotion identification is one of important and challenging tasks in natural language processing. We observe that each rhetoric may express certain evidence of semantic and syntactic patterns. Then, we design a gate mechanism based classification module to capture respective rhetorical representation and identify each rhetoric. Moreover, sentences carved with rhetoric tends to express emotions in subtle ways. We thus propose a new multi-task learning framework that can encode the categorical correlation between tasks to improve the performance of rhetoric and emotion identification problem. Experimental results validate the benefit of the proposed model over state-of-the-art baselines for rhetoric and emotion identification tasks. In addition, a new Chinese rhetorical implicit emotion dataset was constructed and will be released in this work.