  Recurrent Neural Networks (RNNs) have been shown to capture various aspects
of syntax from raw linguistic input. In most previous experiments, however,
learning happens over unrealistic corpora, which do not reflect the type and
amount of data a child would be exposed to. This paper remedies this state of
affairs by training a Long Short-Term Memory network (LSTM) over a
realistically sized subset of child-directed input. The behaviour of the
network is analysed over time using a novel methodology which consists in
quantifying the level of grammatical abstraction in the model's generated
output (its "babbling"), compared to the language it has been exposed to. We
show that the LSTM indeed abstracts new structuresas learning proceeds.
