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# Hexatagging: Projective Dependency Parsing as Tagging

**Authors:** Afra Amini, Tianyu Liu and [Ryan Cotterell](https://md.mova.elamur.ai/free/en/docs/papers/authors/ryan-cotterell/) · **Year:** 2023 · **Venue:** Proceedings of the 61st Annual Meeting of the ACL (Volume 2: Short Papers) **Link:** [aclanthology.org/2023.acl-short.124](https://aclanthology.org/2023.acl-short.124/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**6** citations](https://openalex.org/works?filter=cites:W4385571130) openalex · 2026-10-18[**10** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/acl-2023-amini-hexatagging-projective-dependency-parsing-as/) of the 140673 papers whose bibliographies we read[Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)](https://aclanthology.org/volumes/2023.acl-short/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2023.acl-short.124/)
- [PDF](https://aclanthology.org/2023.acl-short.124.pdf)
- [DOI 10.18653/v1/2023.acl-short.124](https://doi.org/10.18653/v1/2023.acl-short.124)
- [OpenAlex](https://openalex.org/W4385571130)

**Authors (3)**

1. [Afra Amini](https://openalex.org/A5102541943) h 3 · 30 citations · 22 works
2. [Tianyu Liu](https://openalex.org/A5100379419) [ORCID](https://orcid.org/0000-0001-8107-8587) · h 19 · 1352 citations · 104 works
3. [Ryan Cotterell](https://openalex.org/A5061951606) [ORCID](https://orcid.org/0000-0003-4080-1833) · h 45 · 6737 citations · 635 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper turns projective dependency parsing into tagging. Each word receives a "hexatag" from a finite set that encodes the stack actions of a binarized version of its tree, and all words are tagged in parallel with two linear projections over a pretrained encoder. Because validity depends only on the number of stack elements at each step, a dynamic program over stack depth finds the best valid tag sequence exactly in linear time. On the English Penn Treebank the model reaches 96.4 LAS and 97.4 UAS, which the authors report as state of the art for their setting; decoding is about ten times faster than a biaffine parser with the same encoder. On UD 2.2 the trees are made projective with the pseudo-projective transformation. The main limit the authors name is that the model cannot produce non-projective trees.

## Ideas

- I1. Stack-depth decoder beside Eisner: implement the stack-depth dynamic program as a second exact decoder for projective trees in `gparse/src/eisner.rs`'s neighbourhood; the gate is byte-identical trees against Eisner on EWT dev, and then decoding time.
- I2. Tag head in `gparse`: add a linear head that predicts per-word action classes (a hexatag-like set) trained beside the biaffine arc scorer, with the from-scratch four-layer transformer and no pretrained weights; measure EWT test LAS against the current 85.6.
- I3. Projectivity ceiling: count the non-projective gold arcs in EWT test that a projective-only decoder cannot reproduce; this is the upper bound for any tag-based path in Mova and tells whether it is worth building.

## For Mova

The code is `gparse` (the biaffine arc scorer, `gparse/src/eisner.rs`) and, for the rules, `en/src/parse.rs`. The chapter is [20. Syntax: the Universal Dependencies frame](https://md.mova.elamur.ai/free/en/docs/languages/en/20-syntax-overview/). The paper uses a pretrained encoder, which Mova does not use; only the decoding idea and the tag scheme carry over. Measure first: the decode time and the tree agreement on EWT dev.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2023.acl-short.124](https://doi.org/10.18653/v1/2023.acl-short.124)

```
@inproceedings{amini2023hexatagging,
  title = {{Hexatagging: Projective Dependency Parsing as Tagging}},
  author = {Amini, Afra and Liu, Tianyu and Cotterell, Ryan},
  booktitle = {Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2023},
  doi = {10.18653/v1/2023.acl-short.124},
  url = {https://doi.org/10.18653/v1/2023.acl-short.124}
}
```

- [← previous A unified approach to sentence segmentation of punctuated text in many languages](https://md.mova.elamur.ai/free/en/docs/papers/acl-2021-wicks-unified-approach-sentence-segmentation-punctuated/)
- [next → We Understand Elliptical Sentences, and Language Models should Too: A New Dataset for Studying Ellipsis and its Interaction with Thematic Fit](https://md.mova.elamur.ai/free/en/docs/papers/acl-2023-testa-we-understand-elliptical-sentences-language/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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