Constituency Parsing
68 papers with code • 4 benchmarks • 6 datasets
Constituency parsing aims to extract a constituency-based parse tree from a sentence that represents its syntactic structure according to a phrase structure grammar.
Example:
Sentence (S)
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+-------------+------------+
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Noun (N) Verb Phrase (VP)
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John +-------+--------+
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Verb (V) Noun (N)
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sees Bill
Recent approaches convert the parse tree into a sequence following a depth-first traversal in order to be able to apply sequence-to-sequence models to it. The linearized version of the above parse tree looks as follows: (S (N) (VP V N)).
Benchmarks
These leaderboards are used to track progress in Constituency Parsing
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Penn Treebank
FLUE
Taiga Corpus
Most implemented papers
Attention Is All You Need
tensorflow/tensor2tensor
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NeurIPS 2017
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
theamrzaki/text_summurization_abstractive_methods
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NeurIPS 2015
Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning.
Grammar as a Foreign Language
atpaino/deep-text-corrector
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NeurIPS 2015
Syntactic constituency parsing is a fundamental problem in natural language processing and has been the subject of intensive research and engineering for decades.
Recurrent Neural Network Grammars
We introduce recurrent neural network grammars, probabilistic models of sentences with explicit phrase structure.
Constituency Parsing with a Self-Attentive Encoder
nikitakit/self-attentive-parser
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ACL 2018
We demonstrate that replacing an LSTM encoder with a self-attentive architecture can lead to improvements to a state-of-the-art discriminative constituency parser.
Multilingual Constituency Parsing with Self-Attention and Pre-Training
nikitakit/self-attentive-parser
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ACL 2019
We show that constituency parsing benefits from unsupervised pre-training across a variety of languages and a range of pre-training conditions.
Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Autoencoders
iesl/diora
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We introduce deep inside-outside recursive autoencoders (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree.
Generalizing Natural Language Analysis through Span-relation Representations
Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.
YellowFin and the Art of Momentum Tuning
JianGoForIt/YellowFin
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ICLR 2018
We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam.
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance
hantek/distance-parser
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ACL 2018
In this work, we propose a novel constituency parsing scheme.
