ShanghaiTech University Knowledge Management System
Adapting unsupervised syntactic parsing methodology for discourse dependency parsing | |
2021-07-01 | |
会议录名称 | ACL-IJCNLP 2021 - 59TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 11TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING, PROCEEDINGS OF THE CONFERENCE |
页码 | 5782-5794 |
发表状态 | 已发表 |
摘要 | One of the main bottlenecks in developing discourse dependency parsers is the lack of annotated training data. A potential solution is to utilize abundant unlabeled data by using unsupervised techniques, but there is so far little research in unsupervised discourse dependency parsing. Fortunately, unsupervised syntactic dependency parsing has been studied for decades, which could potentially be adapted for discourse parsing. In this paper, we propose a simple yet effective method to adapt unsupervised syntactic dependency parsing methodology for unsupervised discourse dependency parsing. We apply the method to adapt two state-of-the-art unsupervised syntactic dependency parsing methods. Experimental results demonstrate that our adaptation is effective. Moreover, we extend the adapted methods to the semi-supervised and supervised setting and surprisingly, we find that they outperform previous methods specially designed for supervised discourse parsing. Further analysis shows our adaptations result in superiority not only in parsing accuracy but also in time and space efficiency. © 2021 Association for Computational Linguistics |
会议录编者/会议主办者 | Amazon Science ; Apple ; Bloomberg Engineering ; et al. ; Facebook AI ; Google Research |
关键词 | Computational linguistics Annotated training data Dependency parser Dependency parsing Discourse parsing Simple++ Syntactic dependencies Syntactic parsing Two state Unlabeled data Unsupervised techniques |
会议名称 | Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021 |
会议地点 | Virtual, Online |
会议日期 | August 1, 2021 - August 6, 2021 |
收录类别 | EI |
语种 | 英语 |
出版者 | Association for Computational Linguistics (ACL) |
EI入藏号 | 20214611160399 |
EI主题词 | Syntactics |
EI分类号 | 721.1 Computer Theory, Includes Formal Logic, Automata Theory, Switching Theory, Programming Theory |
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/133491 |
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 |
通讯作者 | Tu, Kewei |
作者单位 | 1.School of Information Science and Technology, ShanghaiTech University, China; 2.Shanghai Engineering Research Center of Intelligent Vision and Imaging, China; 3.Shanghai Institute of Microsystem and Information Technology, China; 4.University of Chinese Academy of Sciences, China; 5.Beijing Institute for General Artificial Intelligence, Beijing, China |
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 | Zhang, Liwen,Wang, Ge,Han, Wenjuan,et al. Adapting unsupervised syntactic parsing methodology for discourse dependency parsing[C]//Amazon Science, Apple, Bloomberg Engineering, et al., Facebook AI, Google Research:Association for Computational Linguistics (ACL),2021:5782-5794. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 |
修改评论
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。