# Dependency Graph Parsing as Sequence Labeling

arXiv: https://arxiv.org/abs/2410.17972 · HTML: https://mlsanity.com/paper/2410.17972/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 13 of 13)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Viable Dependency Parsing as Sequence Labeling](https://mlsanity.com/paper/1902.10505/) (2019)
- [Structured Sentiment Analysis as Dependency Graph Parsing](https://mlsanity.com/paper/2105.14504/) (2021)
- [Constituent Parsing as Sequence Labeling](https://mlsanity.com/paper/1810.08994/) (2018)
- [Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://mlsanity.com/paper/1904.09745/) (2019)
- [Bracketing Encodings for 2-Planar Dependency Parsing](https://mlsanity.com/paper/2011.00596/) (2020)
- [A Unifying Theory of Transition-based and Sequence Labeling Parsing](https://mlsanity.com/paper/2011.00584/) (2020)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [Generic Axiomatization of Families of Noncrossing Graphs in Dependency Parsing](https://mlsanity.com/paper/1706.03357/) (2017)

## Built on by (most cited first, 2 of 2)

- [Bringing Emerging Architectures to Sequence Labeling in NLP](https://mlsanity.com/paper/2509.25918/) (2025)
- [Hierarchical Bracketing Encodings Work for Dependency Graphs](https://mlsanity.com/paper/2509.09388/) (2025)

## Abstract (arXiv)

Various linearizations have been proposed to cast syntactic dependency parsing as sequence labeling. However, these approaches do not support more complex graph-based representations, such as semantic dependencies or enhanced universal dependencies, as they cannot handle reentrancy or cycles. By extending them, we define a range of unbounded and bounded linearizations that can be used to cast graph parsing as a tagging task, enlarging the toolbox of problems that can be solved under this paradigm. Experimental results on semantic dependency and enhanced UD parsing show that with a good choice of encoding, sequence-labeling dependency graph parsers combine high efficiency with accuracies close to the state of the art, in spite of their simplicity.