Transition-Based Discourse Parsing with Multilayer Stack Long Short Term Memory
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.
Yanyan Jia Yansong Feng Bingfeng Luo Yuan Ye Tianyang Liu Dongyan Zhao
Institute of Computer Science & Technology,Peking University,Beijing,China
国际会议
第五届自然语言处理与中文计算会议(NLPCC-ICCPOL2016)
昆明
英文
1-13
2016-12-02（万方平台首次上网日期，不代表论文的发表时间）