Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition

TL;DR

This study uses neural models to compare how child-directed and adult-directed language support verb learning, revealing spoken language's broader role rather than CDL-specific optimization.

Contribution

It demonstrates that spoken language, not specifically child-directed speech, enhances verb learning resilience and semantic development in neural models.

Findings

Models trained on spoken language show higher resilience to syntactic disruptions.

Verb meanings emerge before syntactic proficiency, especially in spoken language.

Spoken language's properties, not just CDL, facilitate verb learning.

Abstract

Is child-directed language (CDL) optimized to support language learning, and which aspects of linguistic development does it facilitate? We investigate this question using neural language models trained on CDL versus adult-directed language (ADL). We selectively remove syntactic or lexical co-occurrence information from the model training data, and evaluate the impact of these manipulations on verb meaning acquisition. While disrupting syntax impairs learning across all datasets, models trained on CDL and spoken ADL show significantly higher resilience than those trained on written input. Tracking semantic and syntactic performance over training, we observe a semantic-first trajectory, with verb meanings emerging prior to robust syntactic proficiency, an asynchrony most pronounced in the spoken domain, especially CDL. These results suggest that the advantage for verb learning previously attributed to CDL may instead reflect broader properties of the spoken register, rather than a uniquely CDL-specific optimization.