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	Transition-Based Discourse Parsing with Multilayer Stack Long Short Term Memory
	Jia, Yanyan ; Feng, Yansong ; Luo, Bingfeng ; Ye, Yuan ; Liu, Tianyang ; Zhao, Dongyan
	2016
英文摘要	Discourse parsing aims to identify the relationship between different discourse units, where most previous works focus on recovering the constituency structure among discourse units with carefully designed features. In this paper, we propose to exploit Long Short Term Memory (LSTM) to properly represent discourse units, while using as few feature engineering as possible. Our transition based parsing model features a multilayer stack LSTM framework to discover the dependency structures among different units. Experiments on RST Discourse Treebank show that our model can outperform traditional feature based systems in terms of dependency structures, without complicated feature design. When evaluated in discourse constituency, our parser can also achieve promising performance compared to the state-of-the-art constituency discourse parsers.; National High Technology R&D Program of China [2015AA015403, 2014AA015102]; Natural Science Foundation of China [61202233, 61272344, 61370055]; IBM Research; CPCI-S(ISTP); 360-373; 10102
语种	英语
出处	5th International Conference on Natural Language Processing and Chinese Computing (NLPCC) / 24th International Conference on Computer Processing of Oriental Languages (ICCPOL)
DOI标识	10.1007/978-3-319-50496-4_30
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470117]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Jia, Yanyan,Feng, Yansong,Luo, Bingfeng,et al. Transition-Based Discourse Parsing with Multilayer Stack Long Short Term Memory. 2016-01-01.

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