Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selecting Parallel In-domain Sentences for Neural Machine Translation
  Using Monolingual Texts   Continuously-growing data volumes lead to larger generic models. Specific
use-cases are usually left out, since generic models tend to perform poorly in
domain-specific cases. Our work addresses this gap with a method for selecting
in-domain data from generic-domain (parallel text) corpora, for the task of
machine translation. The proposed method ranks sentences in parallel
general-domain data according to their cosine similarity with a monolingual
domain-specific data set. We then select the top K sentences with the highest
similarity score to train a new machine translation system tuned to the
specific in-domain data. Our experimental results show that models trained on
this in-domain data outperform models trained on generic or a mixture of
generic and domain data. That is, our method selects high-quality
domain-specific training instances at low computational cost and data size.

5
The categories of this paper are: gr-qc
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The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph