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# GenDR: A Generic Deep Realizer with Complex Lexicalization

**Authors:** François Lareau, Florie Lambrey, Ieva Dubinskaite, Daniel Galarreta-Piquette, Maryam Nejat · **Year:** 2018 · **Venue:** Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) **Link:** [aclanthology.org/L18-1478](https://aclanthology.org/L18-1478/) · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — summary only

[**5** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lareau-2018-gendr-generic-deep-realizer-complex/) of the 140673 papers whose bibliographies we read[Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)](https://aclanthology.org/volumes/L18-1/)[licence](https://creativecommons.org/licenses/by-nc/4.0/)

- [Publisher's page](https://aclanthology.org/L18-1478/)
- [PDF](https://aclanthology.org/L18-1478.pdf)

**Authors (5)**

1. François Lareau
2. Florie Lambrey
3. Ieva Dubinskaite
4. Daniel Galarreta-Piquette
5. Maryam Nejat

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

## What it is about

GenDR is a generic deep realiser. Its input is a meaning description with lexical functions, a formalism for the typical word combinations of a language, and it builds the syntactic tree from that description. A salience-driven arborisation step decides which element becomes the head of the output tree, which gives the system a lot of syntactic flexibility. Its main strength, according to the authors, is the handling of collocations: lexical function entries say which word goes with which, and the system uses them in its grammar rules. The team reports about 37,000 described lexical functions, of which about 26,000 are implemented in grammar rules. The authors plan to link the system to verb frame resources and to map its output to UD structures.

## Ideas

- I1. Collocations as counted lexicon entries. Multi-word concepts in the meta-world, such as the translator's `come_to_pass`, are collocations in this sense. Keep each as an entry with its count and its standard form, and report how many entries are used per author.
- I2. Salience as a rule. Choose the head of the output tree by a salience count, for example how often the concept occurs in the author's text, and test whether this choice beats the head given by the reader on dev.
- I3. Report coverage the way the paper does, as described versus implemented. For the translator, state how many meta concepts have a learned standard form and how many fall back to a bare form from `morph`, so that the gaps are visible.

## For Mova

The paper fits the lexicon of `tools/translate` (`meta.rs`, `profile.rs`) and the head choice of `tools/roundtrip`. The lexical-function formalism is not for Mova, but the collocation entries and the coverage report carry over. Note the licence: the paper is NC, so this card is a summary only and no text is taken into the build.

Topics: [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{lareau2018gendr,
  title = {{GenDR: A Generic Deep Realizer with Complex Lexicalization}},
  author = {Lareau, François and Lambrey, Florie and Dubinskaite, Ieva and Galarreta-Piquette, Daniel and Nejat, Maryam},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  url = {https://aclanthology.org/L18-1478/}
}
```

- [← previous The jsRealB Text Realizer: Organization and Use Cases -- Revised version](https://md.mova.elamur.ai/free/en/docs/papers/lapalme-2020-jsrealb-text-realizer-organization-use/)
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- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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