Semi-supervised structured prediction with neural CRF autoencoder

	Semi-supervised structured prediction with neural CRF autoencoder
	Zhang, Xiao 1; Jiang, Yong2 ; Peng, Hao 1; Tu, Kewei2 ; Goldwasser, Dan 1
	2017
会议录名称	2017 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, EMNLP 2017
页码	1701-1711
发表状态	已发表
摘要	In this paper we propose an end-to-end neural CRF autoencoder (NCRF-AE) model for semi-supervised learning of sequential structured prediction problems. Our NCRF-AE consists of two parts: an encoder which is a CRF model enhanced by deep neural networks, and a decoder which is a generative model trying to reconstruct the input. Our model has a unified structure with different loss functions for labeled and unlabeled data with shared parameters. We developed a variation of the EM algorithm for optimizing both the encoder and the decoder simultaneously by decoupling their parameters. Our experimental results over the Part-of-Speech (POS) tagging task on eight different languages, show that the NCRF-AE model can outperform competitive systems in both supervised and semi-supervised scenarios. © 2017 Association for Computational Linguistics.
会议地点	Copenhagen, Denmark
收录类别	EI
出版者	Association for Computational Linguistics (ACL)
EI入藏号	20194207538747
EI主题词	Decoding ; Deep neural networks ; Learning algorithms ; Machine learning ; Signal encoding ; Supervised learning
EI分类号	Information Theory and Signal Processing:716.1 ; Data Processing and Image Processing:723.2
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/29393
专题	信息科学与技术学院_博士生信息科学与技术学院_PI研究组_屠可伟组
作者单位	1.Department of Computer Science, Purdue University, West Lafayette, United States 2.School of Information Science and Technology, ShanghaiTech University, Shanghai, China
推荐引用方式 GB/T 7714	Zhang, Xiao,Jiang, Yong,Peng, Hao,et al. Semi-supervised structured prediction with neural CRF autoencoder[C]:Association for Computational Linguistics (ACL),2017:1701-1711.