# Modelling Child Learning and Parsing of Long-range Syntactic Dependencies

arXiv: https://arxiv.org/abs/2503.12832 · HTML: https://mlsanity.com/paper/2503.12832/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 56 references that are in the map.
- First posted on arXiv 17 Mar 2025.

## Builds on (most cited first, 4 of 4)

- [Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner](https://mlsanity.com/paper/1607.08723/) (2016)
- [Modeling structure-building in the brain with CCG parsing and large language models](https://mlsanity.com/paper/2210.16147/) (2022)
- [Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech](https://mlsanity.com/paper/2109.10952/) (2021)
- [A Language-agnostic Model of Child Language Acquisition](https://mlsanity.com/paper/2408.12254/) (2024)

## Abstract (arXiv)

This work develops a probabilistic child language acquisition model to learn a range of linguistic phenonmena, most notably long-range syntactic dependencies of the sort found in object wh-questions, among other constructions. The model is trained on a corpus of real child-directed speech, where each utterance is paired with a logical form as a meaning representation. It then learns both word meanings and language-specific syntax simultaneously. After training, the model can deduce the correct parse tree and word meanings for a given utterance-meaning pair, and can infer the meaning if given only the utterance. The successful modelling of long-range dependencies is theoretically important because it exploits aspects of the model that are, in general, trans-context-free.