Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training

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	Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training
	Wang, Xinyu1,2,3,4 ; Tu, Kewei1,2,3,4
	2020-12
会议录名称	THE 1ST CONFERENCE OF THE ASIA-PACIFIC CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 10TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (AACL-IJCNLP 2020)
发表状态	正式接收
DOI	/
摘要	In this paper, we propose second-order graphbased neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the accuracy of very recent state-ofthe-art second-order graph-based neural dependency parsers and have significantly faster speed in both training and testing. We also empirically show the advantage of second-order parsing over first-order parsing and observe that the usefulness of the head-selection structured constraint vanishes when using BERT embedding.
会议名称	the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (AACL-IJCNLP 2020)
收录类别	CPCI ; CPCI-S
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/124047
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_屠可伟组信息科学与技术学院_博士生
通讯作者	Tu, Kewei
作者单位	1.School of Information Science and Technology, ShanghaiTech University 2.Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences 3.University of Chinese Academy of Sciences 4.Shanghai Engineering Research Center of Intelligent Vision and Imaging
第一作者单位	信息科学与技术学院
通讯作者单位	信息科学与技术学院
第一作者的第一单位	信息科学与技术学院
推荐引用方式 GB/T 7714	Wang, Xinyu,Tu, Kewei. Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training[C],2020.