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• Paper·arXiv — Language & NLP (cs.CL)·12 May 2026

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

  Francesca Padovani, Jaap Jumelet, Yevgen Matusevych, Arianna Bisazza

  Using neural language models trained on child-directed versus adult-directed language, researchers found that verb meaning acquisition is more resilient to syntactic disruption in spoken language, especially child-directed speech, but the advantage is largely attributable to the spoken register rather than unique properties of child-directed language.

  Original 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.

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