Constituency Grammar Induction
15 papers with code • 1 benchmarks • 1 datasets
Inducing a constituency-based phrase structure grammar.
Benchmarks
These leaderboards are used to track progress in Constituency Grammar Induction
| Trend | Dataset | Best Model | Paper | Code | Compare |
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PTB Diagnostic ECG Database
Most implemented papers
Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
yikangshen/Ordered-Neurons
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ICLR 2019
When a larger constituent ends, all of the smaller constituents that are nested within it must also be closed.
Compound Probabilistic Context-Free Grammars for Grammar Induction
harvardnlp/compound-pcfg
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ACL 2019
We study a formalization of the grammar induction problem that models sentences as being generated by a compound probabilistic context-free grammar.
Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs
sustcsonglin/TN-PCFG
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NAACL 2022
Recent research found it beneficial to use large state spaces for HMMs and PCFGs.
Neural Language Modeling by Jointly Learning Syntax and Lexicon
nyu-mll/PRPN-Analysis
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ICLR 2018
In this paper, We propose 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.
Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
jxhe/struct-learning-with-flow
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EMNLP 2018
In this work, we propose a novel generative model 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.
Unsupervised Recurrent Neural Network Grammars
harvardnlp/urnng
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NAACL 2019
On language modeling, unsupervised RNNGs perform as well their supervised counterparts on benchmarks in English and Chinese.
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders
iesl/diora
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NAACL 2019
We introduce the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree.
Visually Grounded Compound PCFGs
zhaoyanpeng/vpcfg
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EMNLP 2020
In this work, we study visually grounded grammar induction and learn a constituency parser from both unlabeled text and its visual groundings.
PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols
sustcsonglin/TN-PCFG
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NAACL 2021
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.
Neural Bi-Lexicalized PCFG Induction
sustcsonglin/TN-PCFG
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ACL 2021
Neural lexicalized PCFGs (L-PCFGs) have been shown effective in grammar induction.
