Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data Noising as Smoothing in Neural Network Language Models   Data noising is an effective technique for regularizing neural network
models. While noising is widely adopted in application domains such as vision
and speech, commonly used noising primitives have not been developed for
discrete sequence-level settings such as language modeling. In this paper, we
derive a connection between input noising in neural network language models and
smoothing in $n$-gram models. Using this connection, we draw upon ideas from
smoothing to develop effective noising schemes. We demonstrate performance
gains when applying the proposed schemes to language modeling and machine
translation. Finally, we provide empirical analysis validating the relationship
between noising and smoothing.

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
25
The categories of this paper are: patt-sol
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph