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# Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task

**Authors:** [Timothy Dozat](https://md.mova.elamur.ai/free/en/docs/papers/authors/timothy-dozat/), [Peng Qi](https://md.mova.elamur.ai/free/en/docs/papers/authors/peng-qi/), [Christopher D. Manning](https://md.mova.elamur.ai/free/en/docs/papers/authors/christopher-d-manning/) · **Year:** 2017 · **Venue:** Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies **Link:** [aclanthology.org/K17-3002](https://aclanthology.org/K17-3002/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**220** citations](https://openalex.org/works?filter=cites:W2739827909) openalex · 2026-10-19[**125** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/ud-2017-dozat-stanford-s-graph-based-neural/) of the 140673 papers whose bibliographies we read[Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies](https://aclanthology.org/volumes/K17-3/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/K17-3002/)
- [PDF](https://aclanthology.org/K17-3002.pdf)
- [DOI 10.18653/v1/k17-3002](https://doi.org/10.18653/v1/k17-3002)
- [OpenAlex](https://openalex.org/W2739827909)

**Authors (3)**

1. [Timothy Dozat](https://openalex.org/A5046428118) [Semantic Scholar](https://www.semanticscholar.org/author/2277385) · h 15 · 5002 citations · 35 works
2. [Peng Qi](https://openalex.org/A5041658523) [ORCID](https://orcid.org/0000-0003-0514-9464) · [Semantic Scholar](https://www.semanticscholar.org/author/50531624) · h 21 · 6630 citations · 91 works
3. [Christopher D. Manning](https://openalex.org/A5046006076) [ORCID](https://orcid.org/0000-0001-6155-649X) · [Semantic Scholar](https://www.semanticscholar.org/author/144783904) · h 140 · 185462 citations · 781 works

Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart.

## What it is about

The Stanford system is a biaffine graph parser: a BiLSTM encoder, separate ReLU layers for the head and dependent roles of arcs and labels, and biaffine scorers for arcs and labels. Each word vector adds a character-level representation, built by a character LSTM whose hidden states are attended over and whose final cell state is kept, to a pretrained vector and a frequent-word vector. A separate tagger with the same architecture supplies UPOS and XPOS, and each language gets one tagger and one parser trained only on words and tags. The system was ranked first on all five metrics, with a macro average of 93.09 UPOS and 76.30 LAS. The analysis shows that the graph parser's advantage over the transition-based UDPipe 1.1 grows with the non-projectivity of the test set (p < 0.001), that the parser overfits small treebanks, and that the character model helps more as morphological complexity rises.

## Ideas

- I1. gparse: add a character-level word path (char embeddings, a character LSTM, attention over its hidden states plus the final cell state) beside the word embedding, and measure LAS on the EWT dev folds (cv) against the current word-only input.
- I2. gparse: train the parser on its own tagger's predicted tags rather than gold tags, which the paper reports beats UDPipe tags (p < 0.001); measure LAS on EWT dev with gold-tag and predicted-tag training.
- I3. en parse-eval: split EWT dev and test by projectivity and report LAS on the non-projective sentences separately, to see whether the projective arc-standard parser loses accuracy where the paper's graph parser gains.
- I4. gparse/src/eisner.rs: for non-projective output, add a cycle-repair step that picks the best root and fixes cycles as the paper does, and measure LAS on GUM test (test only, NC licence).

## For Mova

The ideas touch the character path and the tag feed of `gparse`, plus the decoder in `gparse/src/eisner.rs`, and the projectivity split in `en parse-eval`. The chapters are `languages/en/21-clause-structure`, `languages/en/25-relative-clauses` (where non-projective attachments live) and `languages/en/29-ellipsis-word-order-information-structure`. Measure first I3, since it is free on the existing parser and shows whether the projective assumption costs anything on English.

Topics: [Dependency parsing](https://md.mova.elamur.ai/free/en/docs/papers/topics/dependency-parsing/) · [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/k17-3002](https://doi.org/10.18653/v1/k17-3002)

```
@inproceedings{dozat2017stanford,
  title = {{Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task}},
  author = {Dozat, Timothy and Qi, Peng and Manning, Christopher D.},
  booktitle = {Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies},
  year = {2017},
  doi = {10.18653/v1/k17-3002},
  url = {https://doi.org/10.18653/v1/k17-3002}
}
```

- [← previous Enhanced UD Dependencies with Neutralized Diathesis Alternation](https://md.mova.elamur.ai/free/en/docs/papers/ud-2017-candito-enhanced-ud-dependencies-neutralized-diathesis/)
- [next → Arc-swift: A Novel Transition System for Dependency Parsing](https://md.mova.elamur.ai/free/en/docs/papers/ud-2017-qi-arc-swift-novel-transition-system/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

18 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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