Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple and Effective Approach to Automatic Post-Editing with Transfer
  Learning   Automatic post-editing (APE) seeks to automatically refine the output of a
black-box machine translation (MT) system through human post-edits. APE systems
are usually trained by complementing human post-edited data with large,
artificial data generated through back-translations, a time-consuming process
often no easier than training an MT system from scratch. In this paper, we
propose an alternative where we fine-tune pre-trained BERT models on both the
encoder and decoder of an APE system, exploring several parameter sharing
strategies. By only training on a dataset of 23K sentences for 3 hours on a
single GPU, we obtain results that are competitive with systems that were
trained on 5M artificial sentences. When we add this artificial data, our
method obtains state-of-the-art results.

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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
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The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-th
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: gr-qc