natural-language-processing-9

Constituency Grammar Induction

3260 papers • 126 benchmarks • 313 datasets

Inducing a constituency-based phrase structure grammar.

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PTB Diagnostic ECG Database

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Penn Treebank

PTB Diagnostic ECG Database

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Most implemented papers

Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks

Aaron C. Courville, Alessandro Sordoni, Shawn Tan, Yikang Shen•Wed Sep 26 2018

The novel recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference.

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Compound Probabilistic Context-Free Grammars for Grammar Induction

Chris Dyer, Alexander M. Rush, Yoon Kim•Sun Jun 23 2019

A formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context free grammar, which is modulated by a per-sentence continuous latent variable, which induces marginal dependencies beyond the traditional context-free assumptions.

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Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs

Kewei Tu, Songlin Yang, Wei Liu•Sat Apr 30 2022

This work uses tensor rank decomposition (aka. CPD) to decrease inference computational complexities for a subset of FGGs subsuming HMMs and PCFGs, and conducts experiments on HMM language modeling and unsupervised PCFG parsing, showing better performance.

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Unsupervised Learning of Syntactic Structure with Invertible Neural Projections

Graham Neubig, Junxian He, Taylor Berg-Kirkpatrick•Tue Jul 31 2018

A novel generative model is proposed that jointly learns discrete syntactic structure and continuous word representations in an unsupervised fashion by cascading an invertible neural network with a structured generative prior so long as the prior is well-behaved.

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Neural Language Modeling by Jointly Learning Syntax and Lexicon

Aaron C. Courville, Zhouhan Lin, Yikang Shen, Chin-Wei Huang•Tue Oct 31 2017

A novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model is proposed.

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Unsupervised Recurrent Neural Network Grammars

Chris Dyer, Lei Yu, Gábor Melis, Alexander M. Rush, A. Kuncoro, Yoon Kim•Sat Apr 06 2019

An inference network parameterized as a neural CRF constituency parser is developed to maximize the evidence lower bound and apply amortized variational inference to unsupervised learning of RNNGs.

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Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders

A. McCallum, Mohit Iyyer, Pat Verga, Andrew Drozdov, Mohit Yadav•Tue Apr 02 2019

DIORA is introduced, a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree that outperforms previously reported results for unsupervised binary constituency parsing on the benchmark WSJ dataset.

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Visually Grounded Compound PCFGs

Ivan Titov, Yanpeng Zhao•Thu Sep 24 2020

This work studies visually grounded grammar induction and learns a constituency parser from both unlabeled text and its visual groundings, and shows that using an extension of probabilistic context-free grammar model, it can do fully-differentiable end-to-end visually grounded learning.

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PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols

Kewei Tu, Songlin Yang, Yanpeng Zhao•Tue Apr 27 2021

A new parameterization form of PCFGs based on tensor decomposition is presented, which has at most quadratic computational complexity in the symbol number and therefore allows us to use a much larger number of symbols.

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Neural Bi-Lexicalized PCFG Induction

Kewei Tu, Songlin Yang, Yanpeng Zhao•Sun May 30 2021

This paper proposes an approach to parameterize L-PCFGs without making implausible independence assumptions, which directly models bilexical dependencies and meanwhile reduces both learning and representation complexities of L- PCFGs.

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