See also NLP Progress - Dependency Parsing

• The paper that started graph-based dependency parsing: McDonald et al 2005 - Non-projective Dependency Parsing using Spanning Tree Algorithms
• Koo & Collins 2010 - Efficient Third-order Dependency Parsers

• Kiperwasser & Goldberg 2016 - Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations First neural graph-based parser. One of the early papers to advocate using BiLSTMs as features in NLP models.
• Zhang et al 2016 - Dependency Parsing as Head Selection Applies MST and Eisner's algorithm to a neural network trained to predict the head of each word.
• Wang & Chang 2016 - Graph-based Dependency Parsing with Bidirectional LSTM Similar and concurrent (but slightly later) than Kiperwasser & Goldberg
• Dozat & Manning 2017 - Deep Biaffine Attention for Neural Dependency Parsing Popular neural graph-based parser, often used as a baseline model.
• Mrini et al 2019 - Rethinking Self-Attention: Towards Interpretability in Neural Parsing
• Gómez-Rodríguez et al 2018 - Global Transition-based Non-projective Dependency Parsing

• Ji et al 2019 - Graph-based Dependency Parsing with Graph Neural Networks
• Zhang et al 2020 - Efficient Second-Order TreeCRF for Neural Dependency Parsing

• Chen & Manning 2014 - A Fast and Accurate Dependency Parser using Neural Networks The first neural dependency parser.
• Ma et al 2018 - Stack-Pointer Networks for Dependency Parsing Has a good overview of existing parsers at the time.
• Keith et al 2018 - Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses

Concise summary of prior work in Nishida 2020.

• Han et al 2020 - A Survey of Unsupervised Dependency Parsing