Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Batch Noise Contrastive Estimation Approach for Training Large
  Vocabulary Language Models   Training large vocabulary Neural Network Language Models (NNLMs) is a
difficult task due to the explicit requirement of the output layer
normalization, which typically involves the evaluation of the full softmax
function over the complete vocabulary. This paper proposes a Batch Noise
Contrastive Estimation (B-NCE) approach to alleviate this problem. This is
achieved by reducing the vocabulary, at each time step, to the target words in
the batch and then replacing the softmax by the noise contrastive estimation
approach, where these words play the role of targets and noise samples at the
same time. In doing so, the proposed approach can be fully formulated and
implemented using optimal dense matrix operations. Applying B-NCE to train
different NNLMs on the Large Text Compression Benchmark (LTCB) and the One
Billion Word Benchmark (OBWB) shows a significant reduction of the training
time with no noticeable degradation of the models performance. This paper also
presents a new baseline comparative study of different standard NNLMs on the
large OBWB on a single Titan-X GPU.

37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg