Robust unsupervised discriminative dependency parsing
Robust unsupervised discriminative dependency parsing
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DOI：
10.26599/tst.2018.9010145
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发表时间：
2020-04
影响因子：
6.6
通讯作者：
Yong Jiang;Jiong Cai;Kewei Tu
中科院分区：
文献类型：
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作者：
Yong Jiang;Jiong Cai;Kewei Tu
判别式方法在无监督依存句法分析中已显示出其有效性。然而，由于其强大的代表性，判别方法往往会在无监督训练期间快速收敛到较差的局部最优值。在本文中，我们通过从强大的深度学习技术中汲取灵感来解决这个问题。具体来说，我们提出了强大的无监督判别依赖解析，一个框架，集成了去噪自动编码器和条件随机场自动编码器的概念。在这个框架内，我们提出了两种类型的句子腐败机制，以及鲁棒训练的后验正则化方法。我们在八种语言上测试了我们的方法，结果表明我们的方法比以前的工作有了显着的改进。© 2020作者（S）。本开放获取期刊上发表的文章均按照知识共享署名4.0国际许可协议（http：//creativecommons.org/licenses/by/4.0/）的条款分发。
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. © 2020 The author(s). The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).