Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Rapid Domain Adaptation for Machine Translation with Monolingual Data   One challenge of machine translation is how to quickly adapt to unseen
domains in face of surging events like COVID-19, in which case timely and
accurate translation of in-domain information into multiple languages is
critical but little parallel data is available yet. In this paper, we propose
an approach that enables rapid domain adaptation from the perspective of
unsupervised translation. Our proposed approach only requires in-domain
monolingual data and can be quickly applied to a preexisting translation system
trained on general domain, reaching significant gains on in-domain translation
quality with little or no drop on general-domain. We also propose an effective
procedure of simultaneous adaptation for multiple domains and languages. To the
best of our knowledge, this is the first attempt that aims to address
unsupervised multilingual domain adaptation.

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The categories of this paper are: physics
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: stat
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: econ