Semantic Graph Convolutional Network for Implicit Discourse Relation Classification

10/21/2019
by   Yingxue Zhang, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/16/2018

Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph

We argue that semantic meanings of a sentence or clause can not be inter...
research
11/05/2018

Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification

Implicit discourse relation classification is one of the most difficult ...
research
04/27/2020

On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification

Implicit discourse relation classification is one of the most difficult ...
research
03/12/2016

Neural Discourse Relation Recognition with Semantic Memory

Humans comprehend the meanings and relations of discourses heavily relyi...
research
04/26/2017

A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations

We introduce an attention-based Bi-LSTM for Chinese implicit discourse r...
research
07/09/2019

Implicit Discourse Relation Identification for Open-domain Dialogues

Discourse relation identification has been an active area of research fo...
research
11/25/2014

One Vector is Not Enough: Entity-Augmented Distributional Semantics for Discourse Relations

Discourse relations bind smaller linguistic units into coherent texts. H...

Please sign up or login with your details

Forgot password? Click here to reset