Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One Billion Word Benchmark for Measuring Progress in Statistical
  Language Modeling   We propose a new benchmark corpus to be used for measuring progress in
statistical language modeling. With almost one billion words of training data,
we hope this benchmark will be useful to quickly evaluate novel language
modeling techniques, and to compare their contribution when combined with other
advanced techniques. We show performance of several well-known types of
language models, with the best results achieved with a recurrent neural network
based language model. The baseline unpruned Kneser-Ney 5-gram model achieves
perplexity 67.6; a combination of techniques leads to 35% reduction in
perplexity, or 10% reduction in cross-entropy (bits), over that baseline.
  The benchmark is available as a code.google.com project; besides the scripts
needed to rebuild the training/held-out data, it also makes available
log-probability values for each word in each of ten held-out data sets, for
each of the baseline n-gram models.

30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th