[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/elder-2020-shape-synth-come-why-we/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization # Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization **Authors:** [Henry Elder](https://md.mova.elamur.ai/free/en/docs/papers/authors/henry-elder/), Robert Burke, [Alexander O'Connor](https://md.mova.elamur.ai/free/en/docs/papers/authors/alexander-o-connor/), [Jennifer Foster](https://md.mova.elamur.ai/free/en/docs/papers/authors/jennifer-foster/) · **Year:** 2020 · **Venue:** Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), pages 7465–7471 **Link:** [aclanthology.org/2020.acl-main.665](https://aclanthology.org/2020.acl-main.665/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology) [**4** citations](https://openalex.org/works?filter=cites:W3023352130) openalex · 2026-10-19[**3** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/elder-2020-shape-synth-come-why-we/) of the 140673 papers whose bibliographies we read[Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics](https://aclanthology.org/volumes/2020.acl-main/)[licence](https://creativecommons.org/licenses/by/4.0/) - [Publisher's page](https://aclanthology.org/2020.acl-main.665/) - [PDF](https://aclanthology.org/2020.acl-main.665.pdf) - [DOI 10.18653/v1/2020.acl-main.665](https://doi.org/10.18653/v1/2020.acl-main.665) - [arXiv 2005.02693](https://arxiv.org/abs/2005.02693) - [OpenAlex](https://openalex.org/W3023352130) **Authors (4)** 1. [Henry Elder](https://openalex.org/A5040570970) [Semantic Scholar](https://www.semanticscholar.org/author/40895152) · h 4 · 135 citations · 10 works 2. Robert Burke [Semantic Scholar](https://www.semanticscholar.org/author/48521531) 3. Alexander O'Connor [Semantic Scholar](https://www.semanticscholar.org/author/2064964383) 4. [Jennifer Foster](https://openalex.org/A5066497689) [ORCID](https://orcid.org/0000-0002-7789-4853) · [Semantic Scholar](https://www.semanticscholar.org/author/144116340) · h 23 · 2298 citations · 125 works Citations and author metrics from openalex (2026-10-19); authors with the same name are not always told apart. ## What it is about Surface realization turns a UD-like tree of lemmas back into a sentence with word order and inflection. The system is a bidirectional LSTM encoder-decoder trained on the English EWT data of the 2018 shared task (12,375 training sentences, 1,978 dev, 2,062 test). Three changes (several random linearisations per tree, scoping brackets, a restricted beam) raise dev BLEU-4 from 57.3 to 72.2. The main claim concerns synthetic data: unlabelled sentences from Wikitext and CNN are parsed, their word order shuffled and forms removed, and about 4.5 million such sentences are added. Test BLEU-4 rises from 72.3 to 80.1, above the earlier best of 72.7; the gain is largest for long sentences. The authors argue that the 2019 ban on synthetic data should be lifted. ## Ideas - I1. `en/src/lin.rs`: make training pairs from unlabelled text, as the paper does. Parse plain sentences with `en/src/parse.rs`, drop the word order and the surface forms, keep the lemmas, and count the word-order tables on the result. Use the same 5 to 50 token and 80% vocabulary filters. Measure the exact-match rate on the EWT dev set, run twice. - I2. Several random orders per tree: for each training tree, count the orders produced by a fixed set of seeds, rather than one order, so that the count tables see the variety of child orders. Compare the dev exact-match rate with one order against three and five, and keep the seed list in the repository. - I3. Error categories from the paper's analysis: exact match, punctuation-only, inflection-only, non-breaking (valid but different order) and breaking (wrong meaning). Add these categories to `tools/roundtrip`, so that each run reports the counts, not just one score. - I4. Report accuracy by sentence length bucket (5–10, 10–20, 20+ tokens) on `en parse-eval` and on the linearizer, since the paper finds the gain concentrated in long sentences. ## For Mova Word order from a tree is `en/src/lin.rs`, and the tree source is `en/src/parse.rs`. The chapters that describe the order rules are [20. Syntax: the Universal Dependencies frame](https://md.mova.elamur.ai/free/en/docs/languages/en/20-syntax-overview/), [21. The simple clause](https://md.mova.elamur.ai/free/en/docs/languages/en/21-clause-structure/) and [29. Ellipsis, word order and information structure](https://md.mova.elamur.ai/free/en/docs/languages/en/29-ellipsis-word-order-information-structure/). The error breakdown belongs in `tools/roundtrip`. The corpora used in the paper are licensed in ways Mova cannot carry (Wikitext is CC BY-SA), so only open text Mova may use should feed I1. Measures: dev exact match, the error breakdown and the length buckets, all run twice for determinism. ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2020.acl-main.665](https://doi.org/10.18653/v1/2020.acl-main.665) · [arXiv 2005.02693](https://arxiv.org/abs/2005.02693) ``` @inproceedings{elder2020shape, title = {{Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization}}, author = {Elder, Henry and Burke, Robert and O'Connor, Alexander and Foster, Jennifer}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, year = {2020}, doi = {10.18653/v1/2020.acl-main.665}, eprint = {2005.02693}, archivePrefix = {arXiv}, url = {https://doi.org/10.18653/v1/2020.acl-main.665} } ``` - [← previous TinyStories: How Small Can Language Models Be and Still Speak Coherent English?](https://md.mova.elamur.ai/free/en/docs/papers/eldan-2023-tinystories-small-language-models-still/) - [next → The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution](https://md.mova.elamur.ai/free/en/docs/papers/emami-2019-knowref-coreference-corpus-removing-gender/) - [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/) ## References 1 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. ## 1. Non-breaking Anja Belz, Michael White, Dominic Espinosa, Eric Kow, Deirdre Hogan, and Amanda Stent. 2011. The First Surface Realisation Shared Task: Overview and Evaluation Results. In Proceedings of the European Workshop on Natural Language Generation, December, pages 217–226. [find](https://scholar.google.com/scholar?q=%23%23+1%2E+Non%2Dbreaking+Anja+Belz%2C+Michael+White%2C+Dominic+Espinosa%2C+Eric+Kow%2C+Deirdre+Hogan%2C+and+Amanda+Stent%2E+2011%2E+The+First+Surface+Realisation+Shared+Task%3A+Overview+and+Evaluation+Results%2E+In+Proceedin) [our card](https://md.mova.elamur.ai/free/en/docs/papers/belz-2011-first-surface-realisation-shared-task/)