# Order matters: Distributional properties of speech to young children bootstraps learning of semantic representations

arXiv: https://arxiv.org/abs/1802.00768 · HTML: https://mlsanity.com/paper/1802.00768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2018.

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

- [Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?](https://mlsanity.com/paper/2511.08199/) (2025)

## Abstract (arXiv)

Some researchers claim that language acquisition is critically dependent on experiencing linguistic input in order of increasing complexity. We set out to test this hypothesis using a simple recurrent neural network (SRN) trained to predict word sequences in CHILDES, a 5-million-word corpus of speech directed to children. First, we demonstrated that age-ordered CHILDES exhibits a gradual increase in linguistic complexity. Next, we compared the performance of two groups of SRNs trained on CHILDES which had either been age-ordered or not. Specifically, we assessed learning of grammatical and semantic structure and showed that training on age-ordered input facilitates learning of semantic, but not of sequential structure. We found that this advantage is eliminated when the models were trained on input with utterance boundary information removed.