Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Sequence Training of Attention Models using Approximative
  Recombination   Sequence discriminative training is a great tool to improve the performance
of an automatic speech recognition system. It does, however, necessitate a sum
over all possible word sequences, which is intractable to compute in practice.
Current state-of-the-art systems with unlimited label context circumvent this
problem by limiting the summation to an n-best list of relevant competing
hypotheses obtained from beam search.
  This work proposes to perform (approximative) recombinations of hypotheses
during beam search, if they share a common local history. The error that is
incurred by the approximation is analyzed and it is shown that using this
technique the effective beam size can be increased by several orders of
magnitude without significantly increasing the computational requirements.
Lastly, it is shown that this technique can be used to effectively perform
sequence discriminative training for attention-based encoder-decoder acoustic
models on the LibriSpeech task.

15
The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph