Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (5): 214-220.doi: 10.11896/j.issn.1002-137X.2019.05.033

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BiLSTM-based Implicit Discourse Relation Classification Combining Self-attention
Mechanism and Syntactic Information

FAN Zi-wei, ZHANG Min, LI Zheng-hua   

  1. (School of Computer Sciences and Technology,Soochow University,Suzhou,Jiangsu 215006,China)
  • Published:2019-05-15

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Abstract: Implicit discourse relation classification is a sub-task in shallow discourse parsing,and it’s also an important task in natural language processing(NLP).Implicit discourse relation is a logic semantic relation inferred from the argument pairsin discourse relations.The analytical results of the implicit discourse relationship can be applied to many na-tural language processing tasks,such as machine translation,automatic document summarization,and questionanswe-ring system.This paper proposed a method based on self-attention mechanism and syntactic information for the classification task of implicit discourse relations.In this method,Bidirectional Long Short-Term Memory Network (BiLSTM) is used to model the inputted argument pairs with syntactic information and express the argument pairs into low-dimension dense vectors.The argument pair information was screened by the self-attention mechanism.At last,this paper conducted experiments on PDTB2.0 dataset.The experimental results show that the proposed model achieves better effects than the baseline system.

Key words: Implicit discourse relation classification, Neural network, Self-attention mechanism, Syntactic information

CLC Number: 

  • TP391
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