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# Towards broad coverage surface realization with CCG

**Authors:** [Michael White](https://md.mova.elamur.ai/free/en/docs/papers/authors/michael-white/), [Rajakrishnan Rajkumar](https://md.mova.elamur.ai/free/en/docs/papers/authors/rajakrishnan-rajkumar/), Scott Martin · **Year:** 2007 · **Venue:** Workshop on Using corpora for natural language generation (MT Summit XI) **Link:** [aclanthology.org/2007.mtsummit-ucnlg.4](https://aclanthology.org/2007.mtsummit-ucnlg.4/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**27** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/white-2007-broad-coverage-surface-realization-ccg/) of the 140673 papers whose bibliographies we read[Proceedings of the Workshop on Using corpora for natural language generation](https://aclanthology.org/volumes/2007.mtsummit-ucnlg/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/2007.mtsummit-ucnlg.4/)
- [PDF](https://aclanthology.org/2007.mtsummit-ucnlg.4.pdf)

**Authors (3)**

1. Michael White
2. Rajakrishnan Rajkumar
3. Scott Martin

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

A progress report on the first broad-coverage English surface realiser for Combinatory Categorial Grammar, built on OpenCCG. The grammar is extracted from CCGbank, a CCG version of the Penn Treebank. The authors revise the derivations, then attach logical forms to categories by XSLT transformations and templates. About 95% of development and 94% of test sentences get a logical form, and about two thirds of them have a single root. Realisation is a hybrid: a chart search, either anytime best-first with a time limit or two-stage packed, ranked by n-gram models over factored words (surface form, part of speech, supertag). Missing words are handled by a smoothing rule that copies the categories of the most frequent words of the same part of speech. On the non-blind development grammar, exact matches reach about 15% and BLEU about 0.66 for the best model; on the blind test section BLEU runs from about 0.37 to about 0.58 across models. The factored models beat word-only trigrams, while the part-of-speech-only and null scorers are weakest.

## Ideas

- I1. Factored counts without a neural model. The translator's standard encoder chooses the most frequent chunk in the most specific context. Add a backoff over factors (word, coarse tag, role) as BTreeMap keys, and measure exact match on dev verses against the current choice.
- I2. A single-root rate per author. The share of verses whose meta-world is one connected tree is a cheap diagnostic of reader attachment noise (open problem 1). Report it per author next to the residual.
- I3. Better fallback for unseen concepts. The paper gives an out-of-vocabulary word the categories of frequent words of its part of speech. Mova gives an unseen concept a bare form from `morph`; try the most frequent chunk among concepts with the same coarse tag and role, and measure on held-out concepts.
- I4. Make the fallback visible. When search fails the paper greedily joins fragments. The translator should report each verse where its output comes from a fallback, so that such verses count against coverage and cannot pass as exact matches.

## For Mova

The code is `tools/translate/src/profile.rs` (encoder), `tools/translate/src/measure.rs` (reporting) and `en/src/morph.rs` (bare forms). The ideas touch open problems 1 (attachment noise), 2 (lexicon noise on unseen concepts) and 6 (corpus weighting). The factor counts in I1 must be built from counts, not from a learned model, to stay within the deterministic rule.

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

```
@inproceedings{white2007broad,
  title = {{Towards broad coverage surface realization with CCG}},
  author = {White, Michael and Rajkumar, Rajakrishnan and Martin, Scott},
  booktitle = {Proceedings of the Workshop on Using corpora for natural language generation},
  year = {2007},
  url = {https://aclanthology.org/2007.mtsummit-ucnlg.4/}
}
```

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## References

31 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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