Is Child-Directed Language Optimized for Word Learning? A Computational Study of Verb Meaning Acquisition
For researchers in language acquisition and computational linguistics, this work challenges the assumption that child-directed speech is specially optimized for word learning, suggesting that spoken register properties are responsible.
The study investigates whether child-directed language (CDL) is optimized for verb learning by training neural language models on CDL versus adult-directed language (ADL) and manipulating syntactic or lexical information. Results show that CDL does not uniquely facilitate verb learning; instead, spoken language in general provides resilience to syntactic disruption, and verb meanings emerge before syntactic proficiency, especially in spoken input.
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.