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- W2963571243 abstract "Some researchers claim that language acquisition is critically dependent on experiencing linguistic input in order of increasing complexity. We set out to test this hypothesis using a simple recurrent neural network (SRN) trained to predict word sequences in CHILDES, a 5-million-word corpus of speech directed to children. First, we demonstrated that age-ordered CHILDES exhibits a gradual increase in linguistic complexity. Next, we compared the performance of two groups of SRNs trained on CHILDES which had either been age-ordered or not. Specifically, we assessed learning of grammatical and semantic structure and showed that training on age-ordered input facilitates learning of semantic, but not of sequential structure. We found that this advantage is eliminated when the models were trained on input with utterance boundary information removed." @default.
- W2963571243 created "2019-07-30" @default.
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- W2963571243 date "2018-02-02" @default.
- W2963571243 modified "2023-09-26" @default.
- W2963571243 title "Order matters: Distributional properties of speech to young children bootstraps learning of semantic representations" @default.
- W2963571243 hasPublicationYear "2018" @default.
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