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# Text Generation from Discourse Representation Structures

**Authors:** [Jiangming Liu](https://md.mova.elamur.ai/free/en/docs/papers/authors/jiangming-liu/), [Shay B. Cohen](https://md.mova.elamur.ai/free/en/docs/papers/authors/shay-b-cohen/), [Mirella Lapata](https://md.mova.elamur.ai/free/en/docs/papers/authors/mirella-lapata/) · **Year:** 2021 · **Venue:** NAACL-HLT 2021 **Link:** [aclanthology.org/2021.naacl-main.35](https://aclanthology.org/2021.naacl-main.35/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**7** citations](https://openalex.org/works?filter=cites:W3173115050) openalex · 2026-10-18[**6** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/liu-2021-text-generation-discourse-representation-structures/) of the 140673 papers whose bibliographies we read[Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies](https://aclanthology.org/volumes/2021.naacl-main/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/2021.naacl-main.35/)
- [PDF](https://aclanthology.org/2021.naacl-main.35.pdf)
- [DOI 10.18653/v1/2021.naacl-main.35](https://doi.org/10.18653/v1/2021.naacl-main.35)
- [OpenAlex](https://openalex.org/W3173115050)

**Authors (3)**

1. [Jiangming Liu](https://openalex.org/A5014019150) [ORCID](https://orcid.org/0000-0002-2178-2385) · h 12 · 952 citations · 40 works
2. [Shay B. Cohen](https://openalex.org/A5030503109) [ORCID](https://orcid.org/0000-0003-4753-8353) · h 28 · 4864 citations · 243 works
3. [Mirella Lapata](https://openalex.org/A5041024491) [ORCID](https://orcid.org/0000-0002-2107-1516) · h 81 · 27884 citations · 495 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The paper generates document text from DRSs on the Groningen Meaning Bank. Two problems make DRSs hard to generate from. First, the conditions of a box form a set, so one meaning can arrive in many orders; the paper learns a canonical order with a graph model (a graph neural network over conditions followed by a pointer network), which beats a counting baseline that orders pairs by frequency. Second, variable names are arbitrary, so the paper renames each variable by its relative position of first appearance, with repeated mentions as relative offsets. The generator is a sequence decoder over a tree encoder, and the encoder uses a sibling tree-LSTM with two gates instead of one per child, which suits DRS trees that can have up to 180 children. Test BLEU is 59.26, above the graph-to-text comparison systems. The order model gains about 9.6 accuracy points over the one-way graph.

## Ideas

- I1. Relative variables: rename meta-token ids by order of first appearance, and write repeated mentions as relative offsets. This makes the surface ids irrelevant and keeps coreference as a count in the meta-world.
- I2. Counting baseline first: order children by counted pairs before any learned ordering. The paper's counting baseline is the reference point; the same test on `en/src/lin.rs` tells whether a richer order model earns its cost.
- I3. Wide frames: a frame with many children should keep its children as a set under one shared table, not as one rule per child. The sibling design shows the memory cost of per-child gates.
- I4. Order invariance: the paper shuffles conditions and renames variables to check that the output does not move. Run the same shuffle on the translator's frames and measure whether the output stays the same.

## For Mova

The ideas fit `tools/translate/meta.rs` (ids and coreference), `en/src/lin.rs` (order) and the frame layer of `prag`. The neural graph model is not for Mova; the counting baseline and the shuffle control are the parts to measure.

Topics: [Discourse analysis](https://md.mova.elamur.ai/free/en/docs/papers/topics/discourse-analysis/) · [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/v1/2021.naacl-main.35](https://doi.org/10.18653/v1/2021.naacl-main.35)

```
@inproceedings{liu2021text,
  title = {{Text Generation from Discourse Representation Structures}},
  author = {Liu, Jiangming and Cohen, Shay B. and Lapata, Mirella},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.35},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.35}
}
```

- [← previous Transition-Based Syntactic Linearization](https://md.mova.elamur.ai/free/en/docs/papers/liu-2015-transition-based-syntactic-linearization/)
- [next → MEANT: An inexpensive, high-accuracy, semi-automatic metric for evaluating translation utility based on semantic roles](https://md.mova.elamur.ai/free/en/docs/papers/lo-2011-meant-inexpensive-high-accuracy-semi/)
- [↑ back to Machine translation](https://md.mova.elamur.ai/free/en/docs/papers/mtrans/)

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