Semantic Graph Convolutional Network for Implicit Discourse Relation Classification

TL;DR

This paper introduces a Semantic Graph Convolutional Network that models deep semantic interactions between discourse arguments, significantly improving implicit discourse relation classification accuracy.

Contribution

The paper proposes a novel SGCN model that captures deeper semantic interactions via graph convolution, advancing implicit discourse relation classification.

Findings

Outperforms previous state-of-the-art models on PDTB and CDTB datasets.

Effectively captures deeper semantic interactions between discourse arguments.

Demonstrates the importance of deep semantic modeling for implicit relation classification.

Abstract

Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations. Modeling the semantic interactions between the two arguments of a relation has proven useful for detecting implicit discourse relations. However, most previous approaches model such semantic interactions from a shallow interactive level, which is inadequate on capturing enough semantic information. In this paper, we propose a novel and effective Semantic Graph Convolutional Network (SGCN) to enhance the modeling of inter-argument semantics on a deeper interaction level for implicit discourse relation classification. We first build an interaction graph over representations of the two arguments, and then automatically extract in-depth semantic interactive information through graph convolution. Experimental results on the English corpus PDTB and the Chinese corpus CDTB both demonstrate the superiority of our model to previous state-of-the-art systems.