Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning to Copy for Automatic Post-Editing   Automatic post-editing (APE), which aims to correct errors in the output of
machine translation systems in a post-processing step, is an important task in
natural language processing. While recent work has achieved considerable
performance gains by using neural networks, how to model the copying mechanism
for APE remains a challenge. In this work, we propose a new method for modeling
copying for APE. To better identify translation errors, our method learns the
representations of source sentences and system outputs in an interactive way.
These representations are used to explicitly indicate which words in the system
outputs should be copied, which is useful to help CopyNet (Gu et al., 2016)
better generate post-edited translations. Experiments on the datasets of the
WMT 2016-2017 APE shared tasks show that our approach outperforms all best
published results.

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The categories of this paper are: hep-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: alg-geom
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The categories of this paper are: funct-an
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: physics