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	A Neural Network Based Translation Constrained Reranking Model for Chinese Dependency Parsing
	Chen, Miaohong ; Chang, Baobao ; Liu, Yang
	2015
关键词	Bilingual dependency parsing Reranking Neural network Machine translation
英文摘要	Bilingual dependency parsing aims to improve parsing performance with the help of bilingual information. While previous work have shown improvements on either or both sides, most of them mainly focus on designing complicated features and rely on golden translations during training and testing. In this paper, we propose a simple yet effective translation constrained reranking model to improve Chinese dependency parsing. The reranking model is trained using a max-margin neural network without any manually designed features. Instead of using golden translations for training and testing, we relax the restrictions and use sentences generated by a machine translation system, which dramatically extends the scope of our model. Experiments on the translated portion of the Chinese Treebank show that our method outperforms the state-of-the-art monolingual Graph/Transition-based parsers by a large margin (UAS).; EI; CPCI-S(ISTP); miaohong-chen@foxmail.com; chbb@pku.edu.cn; cs-ly@pku.edu.cn; 240-249; 9427
语种	英语
出处	CHINESE COMPUTATIONAL LINGUISTICS AND NATURAL LANGUAGE PROCESSING BASED ON NATURALLY ANNOTATED BIG DATA (CCL 2015)
DOI标识	10.1007/978-3-319-25816-4_20
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/436928]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Chen, Miaohong,Chang, Baobao,Liu, Yang. A Neural Network Based Translation Constrained Reranking Model for Chinese Dependency Parsing. 2015-01-01.

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