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Some researchers claim that language acquisition is critically dependent onexperiencing linguistic input in order of increasing complexity. We set out totest this hypothesis using a simple recurrent neural network (SRN) trained topredict word sequences in CHILDES, a 5-million-word corpus of speech directedto children. First, we demonstrated that age-ordered CHILDES exhibits a gradualincrease in linguistic complexity. Next, we compared the performance of twogroups of SRNs trained on CHILDES which had either been age-ordered or not.Specifically, we assessed learning of grammatical and semantic structure andshowed that training on age-ordered input facilitates learning of semantic, butnot of sequential structure. We found that this advantage is eliminated whenthe models were trained on input with utterance boundary information removed.

Order matters: Distributional properties of speech to young children bootstraps learning of semantic representations