Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Lexically Constrained Neural Machine Translation with
  Source-Conditioned Masked Span Prediction   Accurate terminology translation is crucial for ensuring the practicality and
reliability of neural machine translation (NMT) systems. To address this,
lexically constrained NMT explores various methods to ensure pre-specified
words and phrases appear in the translation output. However, in many cases,
those methods are studied on general domain corpora, where the terms are mostly
uni- and bi-grams (>98%). In this paper, we instead tackle a more challenging
setup consisting of domain-specific corpora with much longer n-gram and highly
specialized terms. Inspired by the recent success of masked span prediction
models, we propose a simple and effective training strategy that achieves
consistent improvements on both terminology and sentence-level translation for
three domain-specific corpora in two language pairs.

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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: econ
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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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: nlin
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The categories of this paper are: ao-sci