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# Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization

**Authors:** David L. King, [Michael White](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-white/) · **Year:** 2016 · **Venue:** Proceedings of the 9th International Natural Language Generation conference (INLG 2016) **Link:** [aclanthology.org/W16-6638](https://aclanthology.org/W16-6638/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**1** citations](https://openalex.org/works?filter=cites:W2567493627) openalex-snapshot · 2026-10-14[Proceedings of the 9th International Natural Language Generation conference](https://aclanthology.org/volumes/W16-66/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/W16-6638/)
- [PDF](https://aclanthology.org/W16-6638.pdf)
- [DOI 10.18653/v1/w16-6638](https://doi.org/10.18653/v1/w16-6638)
- [OpenAlex](https://openalex.org/W2567493627)

**Authors (2)**

1. [David L. King](https://openalex.org/A5107229447) [ORCID](https://orcid.org/0000-0003-2253-4241) · [Semantic Scholar](https://www.semanticscholar.org/author/2065220050) · h 5 · 76 citations · 10 works
2. [Michael White](https://openalex.org/A5025147385) [ORCID](https://orcid.org/0000-0002-3062-1719) · [Semantic Scholar](https://www.semanticscholar.org/author/48312837) · h 35 · 11560 citations · 220 works

Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart.

## What it is about

Grammar-based realisers work well when their input carries long-distance dependencies and coordination, but standard UD conversions from the Penn Treebank lose these, because the conversion ignores the traces. The authors take enhanced UD built from the PTB and add the dependencies found in CCGbank, which come from gold PTB traces. Then they adapt an existing CCG induction algorithm so that it can handle UD-style trees, where a phrase is often projected from a dependent such as a determiner rather than from its head. On the test data the induced derivations were complete for about 94 percent of the sentences. The paper reports no realisation scores. It is a progress report: the realisation experiments were still underway when it was written.

## Ideas

- I1. Add shared-argument edges to the parser output. For a verb with a coordinated or long-distance argument, add the second governor as an extra edge in the UD tree, so the frames in `tools/roundtrip` hold the shared argument once for each verb. Count these cases on the English UD data first.
- I2. Report completeness the way the paper does: the share of sentences for which the frame and world reading covers every token. Track it per release, and keep the count deterministic with `BTreeMap` counts.
- I3. Test the coordination case in the translator. A coordinated subject shared by two verbs is one meta token with two heads, which is the head-attachment noise of open problem 1. Pick the coordination verses from the parallel Bible pairs and count how many change attachment across translations.

## More ideas (a second reading)

- I4. `en/src/convert.rs` and the `world` builder: add enhanced edges (the controlled subject, the relative pronoun's antecedent, the free-relative head) as an optional MISC layer, not as a change to the basic tree; measure recall of unbounded-dependency edges on a set we can release, and the LAS on the basic tree, which must stay unchanged.
- I5. `en/src/ud.rs` or a `treeagree` rule: for a free relative clause, make the wh-word the head that the copula points to and remap `ccomp` or `csubj` to the relative; measure the `ccomp` and `csubj` error count on EWT dev before and after.
- I6. Precision protocol: sample 400 added edges, judge each by hand against UD rules, and report the share that the Mova registry gate (`en/src/udreg.rs`) accepts; the paper's 70% exact-label precision is the bar to beat.
- I7. `en/src/lin.rs`: take the enhanced edges as extra input to the word-order generator for relative clauses and free relatives; measure exact word-order match on held-out sentences given gold basic trees, with and without the extra edges.

## For Mova

The paper bears on the parser side and the frames, not on the translator's encoder. The idea that carries over is the enhancement of trees with extra edges. The fit is `en` (parser output), `prag` (frames) and `tools/roundtrip`. Because no realisation result was published, any gain here must be measured from scratch on Mova's own data.

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

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/w16-6638](https://doi.org/10.18653/v1/w16-6638)

```
@inproceedings{king2016enhancing,
  title = {{Enhancing PTB Universal Dependencies for Grammar-Based Surface Realization}},
  author = {King, David L. and White, Michael},
  booktitle = {Proceedings of the 9th International Natural Language Generation conference},
  year = {2016},
  doi = {10.18653/v1/w16-6638},
  url = {https://doi.org/10.18653/v1/w16-6638}
}
```

- [← previous Using Test Suites in Evaluation of Machine Translation Systems](https://md.mova.elamur.ai/free/en/docs/papers/king-1990-test-suites-evaluation-machine-translation/)
- [next → Semi-automatic error analysis for large-scale statistical machine translation](https://md.mova.elamur.ai/free/en/docs/papers/kirchhoff-2007-semi-automatic-error-analysis-large/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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