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

Abstract

We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure. With the goal of improving implicit discourse relation classification, we introduce a paragraph-level neural networks that model inter-dependencies between discourse units as well as discourse relation continuity and patterns, and predict a sequence of discourse relations in a paragraph. Experimental results show that our model outperforms the previous state-of-the-art systems on the benchmark corpus of PDTB.

arXiv commentAccepted by NAACL 2018

References in corpus (5)

• Adam: A Method for Stochastic Optimization
• Sequence to Sequence Learning with Neural Networks
• Improving neural networks by preventing co-adaptation of feature detectors
• A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
• Neural Network Models for Implicit Discourse Relation Classification in English and Chinese without Surface Features

Cited by in corpus (1)

• Semantic Graph Convolutional Network for Implicit Discourse Relation Classification