  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.
