[CaCL] CaCL 1/23: Semantic Graph Convolutional Network for Implicit Discourse Relation Classification

[Oh, Byung-Doh]

Hi everyone,

We'll be discussing the following paper this Thursday.

Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
https://arxiv.org/pdf/1910.09183.pdf
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.

See you all then!
Byung-Doh

=================
Byung-Doh Oh
Ph.D. Student
Department of Linguistics
The Ohio State University
https://byungdoh.github.io
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