Unsupervised Dependency Parsing
4 papers with code • 1 benchmarks • 1 datasets
Unsupervised dependency parsing is the task of inferring the dependency parse of sentences without any labeled training data.
Description from NLP Progress
Benchmarks
These leaderboards are used to track progress in Unsupervised Dependency Parsing
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Penn Treebank
Most implemented papers
StructFormer: Joint Unsupervised Induction of Dependency and Constituency Structure from Masked Language Modeling
google-research/google-research
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ACL 2021
There are two major classes of natural language grammar -- the dependency grammar that models one-to-one correspondences between words and the constituency grammar that models the assembly of one or several corresponded words.
CRF Autoencoder for Unsupervised Dependency Parsing
The encoder part of our model is discriminative and globally normalized which allows us to use rich features as well as universal linguistic priors.
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.
