Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-model Back-translated Distillation for Unsupervised Machine
  Translation   Recent unsupervised machine translation (UMT) systems usually employ three
main principles: initialization, language modeling and iterative
back-translation, though they may apply them differently. Crucially, iterative
back-translation and denoising auto-encoding for language modeling provide data
diversity to train the UMT systems. However, the gains from these
diversification processes has seemed to plateau. We introduce a novel component
to the standard UMT framework called Cross-model Back-translated Distillation
(CBD), that is aimed to induce another level of data diversification that
existing principles lack. CBD is applicable to all previous UMT approaches. In
our experiments, CBD achieves the state of the art in the WMT'14
English-French, WMT'16 English-German and English-Romanian bilingual
unsupervised translation tasks, with 38.2, 30.1, and 36.3 BLEU respectively. It
also yields 1.5-3.3 BLEU improvements in IWSLT English-French and
English-German tasks. Through extensive experimental analyses, we show that CBD
is effective because it embraces data diversity while other similar variants do
not.

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The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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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: physics
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The categories of this paper are: q-fin