# Learned Construction Grammars Converge Across Registers Given Increased Exposure

arXiv: https://arxiv.org/abs/2110.05663 · HTML: https://mlsanity.com/paper/2110.05663/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Mapping Languages: The Corpus of Global Language Use](https://mlsanity.com/paper/2004.00798/) (2020)
- [CxGBERT: BERT meets Construction Grammar](https://mlsanity.com/paper/2011.04134/) (2020)
- [Modeling Global Syntactic Variation in English Using Dialect Classification](https://mlsanity.com/paper/1904.05527/) (2019)
- [Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar](https://mlsanity.com/paper/1904.05529/) (2019)
- [Finding Variants for Construction-Based Dialectometry: A Corpus-Based Approach to Regional CxGs](https://mlsanity.com/paper/2104.01299/) (2021)
- [Global Syntactic Variation in Seven Languages: Towards a Computational Dialectology](https://mlsanity.com/paper/2104.01306/) (2021)
- [Representations of Language Varieties Are Reliable Given Corpus Similarity Measures](https://mlsanity.com/paper/2104.01294/) (2021)
- [A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging](https://mlsanity.com/paper/1412.4021/) (2014)
- [Modeling the Complexity and Descriptive Adequacy of Construction Grammars](https://mlsanity.com/paper/1904.05588/) (2019)
- [Production vs Perception: The Role of Individuality in Usage-Based Grammar Induction](https://mlsanity.com/paper/2104.09033/) (2021)

## Built on by (most cited first, 6 of 6)

- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)
- [Stability of Syntactic Dialect Classification Over Space and Time](https://mlsanity.com/paper/2209.04958/) (2022)
- [Exploring the Constructicon: Linguistic Analysis of a Computational CxG](https://mlsanity.com/paper/2301.12642/) (2023)
- [Emergent Linguistic Structures in Neural Networks are Fragile](https://mlsanity.com/paper/2210.17406/) (2022)
- [Syntactic Variation Across the Grammar: Modelling a Complex Adaptive System](https://mlsanity.com/paper/2309.11869/) (2023)
- [The Computational Learning of Construction Grammars: State of the Art and Prospective Roadmap](https://mlsanity.com/paper/2407.07606/) (2024)

## Abstract (arXiv)

This paper measures the impact of increased exposure on whether learned construction grammars converge onto shared representations when trained on data from different registers. Register influences the frequency of constructions, with some structures common in formal but not informal usage. We expect that a grammar induction algorithm exposed to different registers will acquire different constructions. To what degree does increased exposure lead to the convergence of register-specific grammars? The experiments in this paper simulate language learning in 12 languages (half Germanic and half Romance) with corpora representing three registers (Twitter, Wikipedia, Web). These simulations are repeated with increasing amounts of exposure, from 100k to 2 million words, to measure the impact of exposure on the convergence of grammars. The results show that increased exposure does lead to converging grammars across all languages. In addition, a shared core of register-universal constructions remains constant across increasing amounts of exposure.