Proceedings of the Annual Meeting of the Cognitive Science Society

One proposal for how children acquire syntactic and semantic lexical categories is by inducing them from their distribu-tional signatures in speech. Because the language children are exposed to gradually increases in complexity as they getolder, it is possible that inducing lexical categories from initially simplified speech supports acquisition. We set out to testthis hypothesis using a simple recurrent neural network trained to predict 5 million words of child-directed speech from theAmerican-English portion of the CHILDES database. Evaluation of learned representations showed that models trainedin order in which children actually experience language performed better on a semantic, but not syntactic, categorizationtask. To understand why, we examined how the models encoded words during the earliest stages of training. Our resultsare relevant to important questions in language acquisition, such as the role of early experiences in organizing children’slinguistic representations.