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# RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English

**Authors:** [Stefan Grünewald](https://md.mova.elamur.ai/free/en/docs/papers/authors/stefan-grunewald/), [Annemarie Friedrich](https://md.mova.elamur.ai/free/en/docs/papers/authors/annemarie-friedrich/) · **Year:** 2020 · **Venue:** Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies **Link:** [aclanthology.org/2020.iwpt-1.26](https://aclanthology.org/2020.iwpt-1.26/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**13** citations](https://openalex.org/works?filter=cites:W3037151709) openalex · 2026-10-19[**9** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/ud-2020-grunewald-robertnlp-iwpt-2020-shared-task/) of the 140673 papers whose bibliographies we read[Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies](https://aclanthology.org/volumes/2020.iwpt-1/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2020.iwpt-1.26/)
- [PDF](https://aclanthology.org/2020.iwpt-1.26.pdf)
- [DOI 10.18653/v1/2020.iwpt-1.26](https://doi.org/10.18653/v1/2020.iwpt-1.26)
- [OpenAlex](https://openalex.org/W3037151709)

**Authors (2)**

1. Stefan Grünewald [Semantic Scholar](https://www.semanticscholar.org/author/49948196)
2. [Annemarie Friedrich](https://openalex.org/A5029262106) [Semantic Scholar](https://www.semanticscholar.org/author/33985877) · h 13 · 725 citations · 83 works

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

## What it is about

RobertNLP builds enhanced UD graphs directly, without a basic tree first. A biaffine classifier over fine-tuned RoBERTa embeddings scores every ordered pair of tokens with a label or with "no relation". Labels that carry words (such as obl:instead_of) are replaced by placeholders, for example obl:[case], which cuts the English label set from 399 to 56, and rules put the words back afterwards. A few heuristics add heads to nodes that have none (about 1% of sentences), with a UDify basic parse as the fallback for reachability. The system scores ELAS 88.94 on the English blind test, first among ten systems. The paper also shows that adding training treebanks lowers the score in proportion to their lexical distance from the test data, and that gold tokenization would add about two points (90.80), so segmentation is a large source of loss.

## Ideas

- I1. `gparse`: a delexicalized label set for the enhanced layer (placeholders for case-marked obl and nmod, mark-marked advcl and acl, cc-marked conj), with the relexicalization rules written in Rust, measured by ELAS on the derived layer of EWT dev.
- I2. Before a corpus is added to `gparse` training, compute the Bhattacharyya distance between its vocabulary and the test data (the paper gives 0.142 for EWT and 0.204 for GUM), and add it only if the distance is small; measure on dev.
- I3. When `gparse` outputs a graph with unreachable nodes, repair it with the link from the `en` arc-standard tree, and count repaired sentences per 1k.
- I4. Measure `gparse` LAS on gold tokenization against `en`-tokenized input on dev, to size the segmentation loss (about 2 points in the paper) before investing in `en/src/tok.rs`.

## For Mova

The paper's gains come from a pretrained encoder, which Mova does not use (no foreign pretrained weights), so the transferable part is the architecture: the biaffine scorer and the delexicalized labels in `gparse`, and the validity gates in `tools/treeagree`. The ideas touch [20. Syntax: the Universal Dependencies frame](https://md.mova.elamur.ai/free/en/docs/languages/en/20-syntax-overview/), `22-phrases.md` (multiword case expressions) and `24-coordination-subordination.md`. I4 is the cheapest first measurement.

Topics: [Universal Dependencies](https://md.mova.elamur.ai/free/en/docs/papers/topics/universal-dependencies/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.iwpt-1.26](https://doi.org/10.18653/v1/2020.iwpt-1.26)

```
@inproceedings{grunewald2020robertnlp,
  title = {{RobertNLP at the IWPT 2020 Shared Task: Surprisingly Simple Enhanced UD Parsing for English}},
  author = {Grünewald, Stefan and Friedrich, Annemarie},
  booktitle = {Proceedings of the 16th International Conference on Parsing Technologies and the IWPT 2020 Shared Task on Parsing into Enhanced Universal Dependencies},
  year = {2020},
  doi = {10.18653/v1/2020.iwpt-1.26},
  url = {https://doi.org/10.18653/v1/2020.iwpt-1.26}
}
```

- [← previous Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era](https://md.mova.elamur.ai/free/en/docs/papers/ud-2020-falenska-integrating-graph-based-transition-based/)
- [next → Hybrid Enhanced Universal Dependencies Parsing](https://md.mova.elamur.ai/free/en/docs/papers/ud-2020-heinecke-hybrid-enhanced-universal-dependencies-parsing/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

20 references, 8 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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