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Unsupervised cross-lingual adaptation of dependency parsers using CRF autoencoders | |
2020 | |
会议录名称 | FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS FINDINGS OF ACL: EMNLP 2020 |
卷号 | Findings of the Association for Computational Linguistics: EMNLP 2020 |
页码 | 2127-2133 |
发表状态 | 已发表 |
DOI | / |
摘要 | We consider the task of cross-lingual adaptation of dependency parsers without annotated target corpora and parallel corpora. Previous work either directly applies a discriminative source parser to the target language, ignoring unannotated target corpora, or employs an unsupervised generative parser that can leverage unannotated target data but has weaker representational power than discriminative parsers. In this paper, we propose to utilize unsupervised discriminative parsers based on the CRF autoencoder framework for this task. We train a source parser and use it to initialize and regularize a target parser that is trained on unannotated target data. We conduct experiments that transfer an English parser to 20 target languages. The results show that our method significantly outperforms previous methods. ©2020 Association for Computational Linguistics |
关键词 | Computational linguistics Auto encoders Cross-lingual Dependency parser English parser Parallel corpora Power Target language |
会议名称 | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 |
出版地 | 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA |
会议地点 | Virtual, Online |
会议日期 | November 16, 2020 - November 20, 2020 |
URL | 查看原文 |
收录类别 | EI ; CPCI-S |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61976139] |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods |
WOS记录号 | WOS:001181866501016 |
出版者 | Association for Computational Linguistics (ACL) |
EI入藏号 | 20214511115928 |
EI主题词 | Learning systems |
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/251840 |
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 |
作者单位 | School of Information Science and Technology, ShanghaiTech University Shanghai Engineering, Research Center of Intelligent Vision and Imaging, China |
第一作者单位 | 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 | Li, Zhao,Tu, Kewei. Unsupervised cross-lingual adaptation of dependency parsers using CRF autoencoders[C]. 209 N EIGHTH STREET, STROUDSBURG, PA 18360 USA:Association for Computational Linguistics (ACL),2020:2127-2133. |
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