Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Survey of Domain Adaptation for Neural Machine Translation   Neural machine translation (NMT) is a deep learning based approach for
machine translation, which yields the state-of-the-art translation performance
in scenarios where large-scale parallel corpora are available. Although the
high-quality and domain-specific translation is crucial in the real world,
domain-specific corpora are usually scarce or nonexistent, and thus vanilla NMT
performs poorly in such scenarios. Domain adaptation that leverages both
out-of-domain parallel corpora as well as monolingual corpora for in-domain
translation, is very important for domain-specific translation. In this paper,
we give a comprehensive survey of the state-of-the-art domain adaptation
techniques for NMT.

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The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
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The categories of this paper are: physics
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The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat