# Hierarchical Bracketing Encodings Work for Dependency Graphs

arXiv: https://arxiv.org/abs/2509.09388 · HTML: https://mlsanity.com/paper/2509.09388/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 11 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [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)
- [Constituent Parsing as Sequence Labeling](https://mlsanity.com/paper/1810.08994/) (2018)
- [Second-Order Semantic Dependency Parsing with End-to-End Neural Networks](https://mlsanity.com/paper/1906.07880/) (2019)
- [Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference](https://mlsanity.com/paper/1904.09745/) (2019)
- [Transition-based Semantic Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/2005.13344/) (2020)
- [Discontinuous Constituent Parsing as Sequence Labeling](https://mlsanity.com/paper/2010.00633/) (2020)
- [4 and 7-bit Labeling for Projective and Non-Projective Dependency Trees](https://mlsanity.com/paper/2310.14319/) (2023)
- [Dependency Graph Parsing as Sequence Labeling](https://mlsanity.com/paper/2410.17972/) (2024)
- [Hierarchical Bracketing Encodings for Dependency Parsing as Tagging](https://mlsanity.com/paper/2505.11693/) (2025)

## Abstract (arXiv)

We revisit hierarchical bracketing encodings from a practical perspective in the context of dependency graph parsing. The approach encodes graphs as sequences, enabling linear-time parsing with n tagging actions, and still representing reentrancies, cycles, and empty nodes. Compared to existing graph linearizations, this representation substantially reduces the label space while preserving structural information. We evaluate it on a multilingual and multi-formalism benchmark, showing competitive results and consistent improvements over other methods in exact match accuracy.