Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text normalization using memory augmented neural networks   We perform text normalization, i.e. the transformation of words from the
written to the spoken form, using a memory augmented neural network. With the
addition of dynamic memory access and storage mechanism, we present a neural
architecture that will serve as a language-agnostic text normalization system
while avoiding the kind of unacceptable errors made by the LSTM-based recurrent
neural networks. By successfully reducing the frequency of such mistakes, we
show that this novel architecture is indeed a better alternative. Our proposed
system requires significantly lesser amounts of data, training time and compute
resources. Additionally, we perform data up-sampling, circumventing the data
sparsity problem in some semiotic classes, to show that sufficient examples in
any particular class can improve the performance of our text normalization
system. Although a few occurrences of these errors still remain in certain
semiotic classes, we demonstrate that memory augmented networks with
meta-learning capabilities can open many doors to a superior text normalization
system.

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The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci