# Discontinuous Constituent Parsing as Sequence Labeling

arXiv: https://arxiv.org/abs/2010.00633 · HTML: https://mlsanity.com/paper/2010.00633/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [A Unified Generative Framework for Various NER Subtasks](https://mlsanity.com/paper/2106.01223/) (2021)
- [Paradigm Shift in Natural Language Processing](https://mlsanity.com/paper/2109.12575/) (2021)
- [Unsupervised Discontinuous Constituency Parsing with Mildly Context-Sensitive Grammars](https://mlsanity.com/paper/2212.09140/) (2022)
- [Reducing Discontinuous to Continuous Parsing with Pointer Network Reordering](https://mlsanity.com/paper/2104.06239/) (2021)
- [Discontinuous Grammar as a Foreign Language](https://mlsanity.com/paper/2110.10431/) (2021)
- [Structure-Unified M-Tree Coding Solver for MathWord Problem](https://mlsanity.com/paper/2210.12432/) (2022)
- [From Partial to Strictly Incremental Constituent Parsing](https://mlsanity.com/paper/2402.02782/) (2024)
- [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)

This paper reduces discontinuous parsing to sequence labeling. It first shows that existing reductions for constituent parsing as labeling do not support discontinuities. Second, it fills this gap and proposes to encode tree discontinuities as nearly ordered permutations of the input sequence. Third, it studies whether such discontinuous representations are learnable. The experiments show that despite the architectural simplicity, under the right representation, the models are fast and accurate.