# Global Transition-based Non-projective Dependency Parsing

arXiv: https://arxiv.org/abs/1807.01745 · HTML: https://mlsanity.com/paper/1807.01745/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [Headed-Span-Based Projective Dependency Parsing](https://mlsanity.com/paper/2108.04750/) (2021)
- [AutoAM: An End-To-End Neural Model for Automatic and Universal Argument Mining](https://mlsanity.com/paper/2309.09300/) (2023)
- [Formal Constraints on Dependency Syntax](https://mlsanity.com/paper/2604.04542/) (2026)

## Abstract (arXiv)

Shi, Huang, and Lee (2017) obtained state-of-the-art results for English and Chinese dependency parsing by combining dynamic-programming implementations of transition-based dependency parsers with a minimal set of bidirectional LSTM features. However, their results were limited to projective parsing. In this paper, we extend their approach to support non-projectivity by providing the first practical implementation of the MH4 algorithm, an O(n4) mildly nonprojective dynamic-programming parser with very high coverage on non-projective treebanks. To make MH4 compatible with minimal transition-based feature sets, we introduce a transition-based interpretation of it in which parser items are mapped to sequences of transitions. We thus obtain the first implementation of global decoding for non-projective transition-based parsing, and demonstrate empirically that it is more effective than its projective counterpart in parsing a number of highly non-projective languages