# A Unifying Theory of Transition-based and Sequence Labeling Parsing

arXiv: https://arxiv.org/abs/2011.00584 · HTML: https://mlsanity.com/paper/2011.00584/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 12 of 12)

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- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
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- [Linear-Time Modeling of Linguistic Structure: An Order-Theoretic Perspective](https://mlsanity.com/paper/2305.15057/) (2023)
- [Assessment of Pre-Trained Models Across Languages and Grammars](https://mlsanity.com/paper/2309.11165/) (2023)
- [Dependency Graph Parsing as Sequence Labeling](https://mlsanity.com/paper/2410.17972/) (2024)
- [Parsing linearizations appreciate PoS tags - but some are fussy about errors](https://mlsanity.com/paper/2210.15219/) (2022)
- [Hierarchical Bracketing Encodings for Dependency Parsing as Tagging](https://mlsanity.com/paper/2505.11693/) (2025)
- [The Fragility of Multi-Treebank Parsing Evaluation](https://mlsanity.com/paper/2209.06699/) (2022)
- [On Parsing as Tagging](https://mlsanity.com/paper/2211.07344/) (2022)
- [Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech](https://mlsanity.com/paper/2406.12621/) (2024)
- [Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction](https://mlsanity.com/paper/2412.00208/) (2024)

## Abstract (arXiv)

We define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a theoretical relation between transition-based parsing and sequence-labeling parsing, but also provides a method to obtain new encodings for fast and simple sequence labeling parsing from the many existing transition-based parsers for different formalisms. Applying it to dependency parsing, we implement sequence labeling versions of four algorithms, showing that they are learnable and obtain comparable performance to existing encodings.