---
title: "Learned Construction Grammars Converge Across Registers Given Increased Exposure — Dunn 2021"
description: "The authors test whether construction grammars (CxG) learned from texts of different registers become more similar… 43 references, cited by 3 papers here."
authors: ["Jonathan Dunn", "Harish Tayyar Madabushi"]
year: 2021
venue: "Proceedings of the 25th Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/2021.conll-1.21/"
doi: "10.18653/v1/2021.conll-1.21"
arxiv: "2110.05663"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dunn-2021-learned-construction-grammars-converge-across/"
---
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# Learned Construction Grammars Converge Across Registers Given Increased Exposure

**Authors:** [Jonathan Dunn](https://md.research.elamur.ai/free/en/authors/jonathan-dunn/) and [Harish Tayyar Madabushi](https://md.research.elamur.ai/free/en/authors/harish-tayyar-madabushi/) · **Year:** 2021 · **Venue:** Proceedings of the 25th Conference on Computational Natural Language Learning · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.conll-1.21/) · [PDF](https://aclanthology.org/2021.conll-1.21.pdf) · [DOI 10.18653/v1/2021.conll-1.21](https://doi.org/10.18653/v1/2021.conll-1.21) · [arXiv 2110.05663](https://arxiv.org/abs/2110.05663) · [OpenAlex](https://openalex.org/W3207430422)

**Citations:** 8 citations (OpenAlex, 2026-10-14) · 3 in our library, of the 140,884 papers whose bibliographies we read

## Summary

The authors test whether construction grammars (CxG) learned from texts of different registers become more similar as the model receives more data. Register noticeably changes the frequency of constructions. Therefore, learning from different sources could in principle produce different grammars. To test this, the researchers simulated language acquisition for 12 languages: six Germanic and six Romance. For each language, they used corpora from three registers: Twitter, Wikipedia, and web texts from Common Crawl. The amount of input data was gradually increased from 100 thousand to 2 million words, yielding a series of grammars for each register. Constructions were extracted with the algorithm of Dunn (2017), which combines the ΔP association measure, beam search, and the MDL criterion. Grammar similarity was measured with two variants of the Jaccard coefficient. The fuzzy variant also takes rare constructions into account, while the frequency-weighted variant focuses on the core of the grammar. In all languages, the shared portion of the register grammars grew as data were added, although the rate and limit of convergence varied. A core of frequent constructions shared by all registers formed already at small data sizes and changed very little thereafter. This shows how general syntactic regularities and register variation combine.

## Key points

- Data: 2 million words for each of the three registers (TW, WK, CC) for each of the 12 languages. The Germanic set comprises Danish, Dutch, English, German, Norwegian, and Swedish. Romance: Catalan, French, Italian, Portuguese, Romanian, Spanish.
- Learning proceeded in increments of 100 thousand words, yielding 20 grammars for each register. Three register pairs were compared: CC–TW, CC–WK, and WK–TW.
- Experiment 1 (fuzzy Jaccard): in the Germanic languages, similarity rises from roughly 0.2 to 0.4. The Romance languages are generally more similar across registers: French shows the highest value, Romanian the lowest. Wikipedia and the web are consistently closer to each other than to Twitter.
- Experiment 2 (weighted Jaccard): the similarity of the grammar core is much higher, roughly 0.7 to 0.8. Among the Germanic languages, it is lowest for Dutch and highest for Swedish; among the Romance languages, it is lowest for Spanish and highest for Portuguese. After a few hundred thousand words, the core barely changes.
- Exceptions: in Norwegian, convergence continues throughout. In Romanian, similarity first decreases, and the jump occurs much later.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

## Institutions

- [University of Sheffield](https://md.research.elamur.ai/free/en/universities/university-of-sheffield/) — University, Sheffield, United Kingdom · 67 works · 17,924 citations
- [University of Canterbury](https://md.research.elamur.ai/free/en/universities/university-of-canterbury/) — University, Christchurch, New Zealand · 2 works · 246 citations

## Cite

Cite as: Jonathan Dunn and Harish Tayyar Madabushi. 2021. Learned Construction Grammars Converge Across Registers Given Increased Exposure. Proceedings of the 25th Conference on Computational Natural Language Learning.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dunn2021learned,
  title = {{Learned Construction Grammars Converge Across Registers Given Increased Exposure}},
  author = {Jonathan Dunn and Harish Tayyar Madabushi},
  booktitle = {Proceedings of the 25th Conference on Computational Natural Language Learning},
  year = {2021},
  doi = {10.18653/v1/2021.conll-1.21},
  eprint = {2110.05663},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2021.conll-1.21}
}
```

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