Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
This work addresses a key bottleneck in discourse parsing for natural language processing, offering an incremental improvement over existing methods.
The paper tackles the problem of implicit discourse relation classification, which is challenging due to the lack of explicit connectives, by proposing a Semantic Graph Convolutional Network (SGCN) to model deeper semantic interactions between arguments, achieving state-of-the-art results on PDTB and CDTB corpora.
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.