Robust Unsupervised Discriminative Dependency Parsing

被引：0

作者：

Yong Jiang ^{[1
,2
,3
]}

Jiong Cai ^{[1
,2
,3
]}

Kewei Tu ^{[1
]}

机构：

[1] the School of Information Science and Technology,ShanghaiTech University

[2] Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences

[3] University of Chinese Academy of Sciences

来源：

TsinghuaScienceandTechnology | 2020年 / 25卷 / 02期

基金：

中国国家自然科学基金;

关键词：

unsupervised learning; dependency parsing; autoencoders;

D O I：

暂无

中图分类号：

TP391.1 [文字信息处理];

学科分类号：

摘要：

Discriminative approaches have shown their effectiveness in unsupervised dependency parsing.However,due to their strong representational power,discriminative approaches tend to quickly converge to poor local optima during unsupervised training.In this paper,we tackle this problem by drawing inspiration from robust deep learning techniques.Specifically,we propose robust unsupervised discriminative dependency parsing,a framework that integrates the concepts of denoising autoencoders and conditional random field autoencoders.Within this framework,we propose two types of sentence corruption mechanisms as well as a posterior regularization method for robust training.We tested our methods on eight languages and the results show that our methods lead to significant improvements over previous work.

引用

页码：192 / 202

页数：11

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