Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Reranking Improves Subjective Quality of Machine Translation:
  NAIST at WAT2015   This year, the Nara Institute of Science and Technology (NAIST)'s submission
to the 2015 Workshop on Asian Translation was based on syntax-based statistical
machine translation, with the addition of a reranking component using neural
attentional machine translation models. Experiments re-confirmed results from
previous work stating that neural MT reranking provides a large gain in
objective evaluation measures such as BLEU, and also confirmed for the first
time that these results also carry over to manual evaluation. We further
perform a detailed analysis of reasons for this increase, finding that the main
contributions of the neural models lie in improvement of the grammatical
correctness of the output, as opposed to improvements in lexical choice of
content words.

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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: nlin
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an