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.
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).
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We therefore propose Cross-View Training (CVT), a semi-supervised learning algorithm that improves the representations of a Bi-LSTM sentence encoder using a mix of labeled and unlabeled data.
SOTA for CCG Supertagging on CCGBank
In this work, we present a compact, modular framework for constructing novel recurrent neural architectures.
Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models.
#4 best model for Dependency Parsing on Penn Treebank
We measure the performance of CamemBERT compared to multilingual models in multiple downstream tasks, namely part-of-speech tagging, dependency parsing, named-entity recognition, and natural language inference.
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.
#3 best model for Dependency Parsing on The following results are just for references
Learning effective representations of sentences is one of the core missions of natural language understanding.
Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive.
SOTA for Sentence Classification on Paper Field (using extra training data)
CITATION INTENT CLASSIFICATION DEPENDENCY PARSING LANGUAGE MODELLING MEDICAL NAMED ENTITY RECOGNITION PARTICIPANT INTERVENTION COMPARISON OUTCOME EXTRACTION RELATION EXTRACTION SENTENCE CLASSIFICATION