# Viable Dependency Parsing as Sequence Labeling

arXiv: https://arxiv.org/abs/1902.10505 · HTML: https://mlsanity.com/paper/1902.10505/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 30 of 34)

- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021](https://mlsanity.com/paper/2106.04216/) (2021)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [Bertinho: Galician BERT Representations](https://mlsanity.com/paper/2103.13799/) (2021)
- [Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing](https://mlsanity.com/paper/2108.07556/) (2021)
- [Training dataset and dictionary sizes matter in BERT models: the case of Baltic languages](https://mlsanity.com/paper/2112.10553/) (2021)
- [Replicating and Extending "Because Their Treebanks Leak": Graph Isomorphism, Covariants, and Parser Performance](https://mlsanity.com/paper/2106.00352/) (2021)
- [Evaluation of contextual embeddings on less-resourced languages](https://mlsanity.com/paper/2107.10614/) (2021)
- [Structure-Unified M-Tree Coding Solver for MathWord Problem](https://mlsanity.com/paper/2210.12432/) (2022)
- [Hexatagging: Projective Dependency Parsing as Tagging](https://mlsanity.com/paper/2306.05477/) (2023)
- [Structural Ambiguity and its Disambiguation in Language Model Based Parsers: the Case of Dutch Clause Relativization](https://mlsanity.com/paper/2305.14917/) (2023)
- [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)
- [On the Challenges of Fully Incremental Neural Dependency Parsing](https://mlsanity.com/paper/2309.16254/) (2023)
- [What has LeBenchmark Learnt about French Syntax?](https://mlsanity.com/paper/2403.02173/) (2024)
- [A Syntax-Injected Approach for Faster and More Accurate Sentiment Analysis](https://mlsanity.com/paper/2406.15163/) (2024)
- [Dancing in the syntax forest: fast, accurate and explainable sentiment analysis with SALSA](https://mlsanity.com/paper/2406.16071/) (2024)
- [Dependency Graph Parsing as Sequence Labeling](https://mlsanity.com/paper/2410.17972/) (2024)
- [Parsing the Switch: LLM-Based UD Annotation for Complex Code-Switched and Low-Resource Languages](https://mlsanity.com/paper/2506.07274/) (2025)
- [Splitting EUD graphs into trees: A quick and clatty approach](https://mlsanity.com/paper/2106.13155/) (2021)
- [Parsing linearizations appreciate PoS tags - but some are fussy about errors](https://mlsanity.com/paper/2210.15219/) (2022)
- [When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality](https://mlsanity.com/paper/2402.13113/) (2024)
- [Hierarchical Bracketing Encodings for Dependency Parsing as Tagging](https://mlsanity.com/paper/2505.11693/) (2025)
- [Bringing Emerging Architectures to Sequence Labeling in NLP](https://mlsanity.com/paper/2509.25918/) (2025)
- [Schema-Free Dependency Parsing via Sequence Generation](https://mlsanity.com/paper/2201.12407/) (2022)
- [Cross-lingual Inflection as a Data Augmentation Method for Parsing](https://mlsanity.com/paper/2205.09350/) (2022)
- [The Fragility of Multi-Treebank Parsing Evaluation](https://mlsanity.com/paper/2209.06699/) (2022)
- [The Impact of Edge Displacement Vaserstein Distance on UD Parsing Performance](https://mlsanity.com/paper/2209.07139/) (2022)
- [On Parsing as Tagging](https://mlsanity.com/paper/2211.07344/) (2022)
- [Textless Dependency Parsing by Labeled Sequence Prediction](https://mlsanity.com/paper/2407.10118/) (2024)

## Abstract (arXiv)

We recast dependency parsing as a sequence labeling problem, exploring several encodings of dependency trees as labels. While dependency parsing by means of sequence labeling had been attempted in existing work, results suggested that the technique was impractical. We show instead that with a conventional BiLSTM-based model it is possible to obtain fast and accurate parsers. These parsers are conceptually simple, not needing traditional parsing algorithms or auxiliary structures. However, experiments on the PTB and a sample of UD treebanks show that they provide a good speed-accuracy tradeoff, with results competitive with more complex approaches.