# The Computational Learning of Construction Grammars: State of the Art and Prospective Roadmap

arXiv: https://arxiv.org/abs/2407.07606 · HTML: https://mlsanity.com/paper/2407.07606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 85 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning](https://mlsanity.com/paper/1612.06890/) (2016)
- [The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative](https://mlsanity.com/paper/2210.13181/) (2022)
- [CxGBERT: BERT meets Construction Grammar](https://mlsanity.com/paper/2011.04134/) (2020)
- [Construction Grammar Provides Unique Insight into Neural Language Models](https://mlsanity.com/paper/2302.02178/) (2023)
- [Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar](https://mlsanity.com/paper/1904.05529/) (2019)
- [Modeling the Complexity and Descriptive Adequacy of Construction Grammars](https://mlsanity.com/paper/1904.05588/) (2019)
- [Learned Construction Grammars Converge Across Registers Given Increased Exposure](https://mlsanity.com/paper/2110.05663/) (2021)
- [Bottleneck Low-rank Transformers for Low-resource Spoken Language Understanding](https://mlsanity.com/paper/2206.14318/) (2022)
- [Exposure and Emergence in Usage-Based Grammar: Computational Experiments in 35 Languages](https://mlsanity.com/paper/2211.14160/) (2022)
- [Construction Grammar and Artificial Intelligence](https://mlsanity.com/paper/2309.00135/) (2023)
- [Exploring the Constructicon: Linguistic Analysis of a Computational CxG](https://mlsanity.com/paper/2301.12642/) (2023)
- [Construction Grammar and Language Models](https://mlsanity.com/paper/2308.13315/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Method for Learning Large-Scale Computational Construction Grammars from Semantically Annotated Corpora](https://mlsanity.com/paper/2603.12754/) (2026)

## Abstract (arXiv)

This paper documents and reviews the state of the art concerning computational models of construction grammar learning. It brings together prior work on the computational learning of form-meaning pairings, which has so far been studied in several distinct areas of research. The goal of this paper is threefold. First of all, it aims to synthesise the variety of methodologies that have been proposed to date and the results that have been obtained. Second, it aims to identify those parts of the challenge that have been successfully tackled and reveal those that require further research. Finally, it aims to provide a roadmap which can help to boost and streamline future research efforts on the computational learning of large-scale, usage-based construction grammars.