  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.
