Dependency parsing

Dependency parsing is the task of extracting a dependency parse of a sentence that represents its grammatical structure and defines the relationships between “head” words and words, which modify those heads.

Example:

     root
      |
      | +-------dobj---------+
      | |                    |
nsubj | |   +------det-----+ | +-----nmod------+
+--+  | |   |              | | |               |
|  |  | |   |      +-nmod-+| | |      +-case-+ |
+  |  + |   +      +      || + |      +      | |
I  prefer  the  morning   flight  through  Denver

Relations among the words are illustrated above the sentence with directed, labeled arcs from heads to dependents (+ indicates the dependent).

Penn Treebank

Models are evaluated on the Stanford Dependency conversion (v3.3.0) of the Penn Treebank with predicted POS-tags. Punctuation symbols are excluded from the evaluation. Evaluation metrics are unlabeled attachment score (UAS) and labeled attachment score (LAS). Here, we also mention the predicted POS tagging accuracy.

Model	POS	UAS	LAS	Paper / Source	Code
Deep Biaffine by Dozat and Manning (2017)	97.3	95.44	93.76	Deep Biaffine Attention for Neural Dependency Parsing	Official
jPTDP by Nguyen and Verspoor (2018)	97.97	94.51	92.87	An improved neural network model for joint POS tagging and dependency parsing	Official
by Andor et al. (2016)	97.44	94.61	92.79	Globally Normalized Transition-Based Neural Networks
Distilled neural FOG by Kuncoro et al. (2016)	97.3	94.26	92.06	Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser
by Weiss et al. (2015)	97.44	93.99	92.05	Structured Training for Neural Network Transition-Based Parsing
BIST transition-based parser by Kiperwasser and Goldberg (2016)	97.3	93.9	91.9	Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations	Official
Arc-hybrid by Ballesteros et al. (2016)	97.3	93.56	91.42	Training with Exploration Improves a Greedy Stack-LSTM Parser
BIST graph-based parser by Kiperwasser and Goldberg (2016)	97.3	93.1	91.0	Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations	Official

The following results are just for references:

Model	POS	UAS	comment	Paper / Source	Code
Stack-only RNNG by Kuncoro et al. (2017)		95.8	Constituent parser	What Do Recurrent Neural Network Grammars Learn About Syntax?
Semi-supervised LSTM-LM by Choe and Charniak (2016)		95.9	Constituent parser	Parsing as Language Modeling
Deep Biaffine by Dozat and Manning (2017)	97.3	95.66	Stanford conversion **v3.5.0**	Deep Biaffine Attention for Neural Dependency Parsing	Official

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