Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Minimum Risk Training for Neural Machine Translation   We propose minimum risk training for end-to-end neural machine translation.
Unlike conventional maximum likelihood estimation, minimum risk training is
capable of optimizing model parameters directly with respect to arbitrary
evaluation metrics, which are not necessarily differentiable. Experiments show
that our approach achieves significant improvements over maximum likelihood
estimation on a state-of-the-art neural machine translation system across
various languages pairs. Transparent to architectures, our approach can be
applied to more neural networks and potentially benefit more NLP tasks.

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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
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The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga