Interactive Capsule Networks with a Novel Dynamic Routing Mechanism for Implicit Discourse Relation Recognition

Jing Xu, Ruifang He, Haodong Zhao

Abstract: Implicit discourse relation recognition aims to understand and infer the relations between discourse arguments, which is a classification task. Existing models mostly model the interaction of discourse arguments from the perspective of a single relation, which may fail to learn the complicated interaction patterns between two arguments under different discourse relations. In this paper, we propose a novel neural approach based on capsule networks with dynamic interactive routing mechanism, named Interactive Capsule Networks (ICN) model to address the issues. It dynamically maps argument feature capsules to discourse relation capsules in an iterative refinement manner, which can mine the discourse relation-indicative semantic clues of argument features. Then, the designed dynamic interactive routing mechanism captures the more comprehensive argument interaction from the perspective of multiple relations. The experimental results on the Penn Discourse TreeBank (PDTB) demonstrate the effectiveness of our proposed ICN model.