214 papers with code • 12 benchmarks • 7 datasets
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).
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.
Ranked #1 on CCG Supertagging on CCGbank
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.
Ranked #15 on Dependency Parsing on Penn Treebank
In addition, knowledge distillation where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher.
This paper builds off recent work from Kiperwasser & Goldberg (2016) using neural attention in a simple graph-based dependency parser.
Ranked #2 on Dependency Parsing on CoNLL-2009
This paper describes our system (HIT-SCIR) submitted to the CoNLL 2018 shared task on Multilingual Parsing from Raw Text to Universal Dependencies.
Ranked #3 on Dependency Parsing on Universal Dependencies