Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent babbling: evaluating the acquisition of grammar from limited
  input data   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.

12
The categories of this paper are: nucl-th
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: q-fin
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The categories of this paper are: eess
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th