PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols
2021-04-28
状态已发表
摘要

Probabilistic context-free grammars (PCFGs) with neural parameterization have been shown to be effective in unsupervised phrase-structure grammar induction. However, due to the cubic computational complexity of PCFG representation and parsing, previous approaches cannot scale up to a relatively large number of (nonterminal and preterminal) symbols. In this work, we present a new parameterization form of PCFGs based on tensor decomposition, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols. We further use neural parameterization for the new form to improve unsupervised parsing performance. We evaluate our model across ten languages and empirically demonstrate the effectiveness of using more symbols. 

DOIarXiv:2104.13727
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出处Arxiv
WOS记录号PPRN:11721387
WOS类目Computer Science, Interdisciplinary Applications
资助项目National Natural Science Foundation of China[61976139]
文献类型预印本
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348570
专题信息科学与技术学院
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_硕士生
作者单位
1.Shanghai Tech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
2.Univ Chinese Acad Sci, Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai Engn Res Ctr Intelligent Vis & Imaging, Shanghai, Peoples R China
3.Univ Edinburgh, ILCC, Edinburgh, Scotland
推荐引用方式
GB/T 7714
Yang, Songlin,Zhao, Yanpeng,Tu, Kewei. PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols. 2021.
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