Child-Directed Speech Facilitates Semantic Role Learning: A Machine Learning Approach.

Overview

1. Institute for the Interdisciplinary Study of Language Evolution, University of Zurich, Zurich, Switzerland
2. Department of Linguistics, University of Cologne, Cologne, Germany

Abstract

Semantic roles, namely the agent (‘doer’) and patient (‘undergoer’) roles, are fundamental to language acquisition, as they enable learners to map meaning onto syntactic structure. Grammars typically impose a binary classification on how these roles map onto syntactic functions. These functions are encoded through features such as agreement, case marking and word order, which children gradually acquire through exposure to their linguistic environment. Notably, child-directed speech constitutes a primary source of linguistic input during acquisition (Hart & Risley, 1995; Weisleder & Fernald, 2013). At present, little is known about the effects of child-directed speech on the learning of semantic roles and even less in languages with diverse grammatical features. Here, we investigate whether child-directed speech facilitates the learning of semantic roles, specifically examining whether it enhances semantic role interpretation compared to adult-directed speech. We examine English and Russian, two languages, which differ fundamentally in how they encode semantic roles, thereby presenting distinct challenges for the language-learning child. We use artificial neural language models to analyse the statistical properties of naturalistic child-directed and adult-directed speech, testing which register more effectively facilitates semantic role learning. In Study 1, we examine whether semantic roles are more easily classified in naturalistic utterances from child-directed speech than adult-directed speech. In Study 2, we test which register better supports learning and generalising semantic roles by evaluating language models trained on either register on the same controlled test set. Study 1 shows that semantic roles are more easily classified in child-directed speech than adult-directed speech, with a more pronounced effect in Russian than in English. This suggests that child-directed speech may be optimised more strongly in a language where semantic roles are expressed in more varied forms and positions, as is the case in Russian. Study 2 shows that the knowledge of semantic roles can be generalised by the models to structures that do not frequently occur in either child-directed speech or adult-directed speech, and that, on the whole, this is more successful based on input from child-directed speech than adult-directed speech in both languages. Our results provide first evidence that child-directed speech is tailored to the language-specific needs of children, facilitating the acquisition of semantic roles, which are a prerequisite for the acquisition of syntax. These findings show that child-directed speech actively supports the acquisition of of semantic roles, helping children map meaning onto syntactic structure.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• osf:4tevz, at OSF; found in “DATA AVAILABILITY STATEMENT”

Data availability statement

The English data is available on the OSF repository (https://osf.io/4tevz/?view_only=9c5af7e3cd9e493a886808e7c47fc206). The Russian data can be shared upon request through the ACQDIV Corpus (https://www.isle.uzh.ch/en/ACQDIV/resources.html).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 3 authors, 4 keywords, 1 funder, 95 references.

Cite

This paper

Huber, E., Bickel, B., & Stoll, S. (2026). Child-Directed Speech Facilitates Semantic Role Learning: A Machine Learning Approach. Open mind : discoveries in cognitive science, 10, 641-672. https://doi.org/10.1162/opmi.a.351

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