Transition-Based Dependency Parsing
12 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Transition-Based Dependency Parsing
Latest papers
Algorithms for Weighted Pushdown Automata
Weighted pushdown automata (WPDAs) are at the core of many natural language processing tasks, like syntax-based statistical machine translation and transition-based dependency parsing.
Graph-to-Graph Transformer for Transition-based Dependency Parsing
alirezamshi/G2GTr
•
•
Findings of the Association for Computational Linguistics 2020
We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing.
Bidirectional Transition-Based Dependency Parsing
Traditionally, a transitionbased dependency parser processes an input sentence and predicts a sequence of parsing actions in a left-to-right manner.
Joint Learning of POS and Dependencies for Multilingual Universal Dependency Parsing
bcmi220/joint_stackptr
•
•
CONLL 2018
This paper describes the system of team LeisureX in the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies.
Tree-Stack LSTM in Transition Based Dependency Parsing
We introduce tree-stack LSTM to model state of a transition based parser with recurrent neural networks.
Distilling Knowledge for Search-based Structured Prediction
Many natural language processing tasks can be modeled into structured prediction and solved as a search problem.
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set
We first present a minimal feature set for transition-based dependency parsing, continuing a recent trend started by Kiperwasser and Goldberg (2016a) and Cross and Huang (2016a) of using bi-directional LSTM features.
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly.
Efficient Structured Inference for Transition-Based Parsing with Neural Networks and Error States
Transition-based approaches based on local classification are attractive for dependency parsing due to their simplicity and speed, despite producing results slightly below the state-of-the-art.
