Recent advances of low-resource neural machine translation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10439921" target="_blank" >RIV/00216208:11320/21:10439921 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=x7qHcKxrmZ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=x7qHcKxrmZ</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10590-021-09281-1" target="_blank" >10.1007/s10590-021-09281-1</a>
Alternative languages
Result language
angličtina
Original language name
Recent advances of low-resource neural machine translation
Original language description
In recent years, neural network-based machine translation (MT) approaches have steadily superseded the statistical MT (SMT) methods, and represents the current state-of-the-art in MT research. Neural MT (NMT) is a data-driven end-to-end learning protocol whose training routine usually requires a large amount of parallel data in order to build a reasonable-quality MT system. This is particularly problematic for those language pairs that do not have enough parallel text for training. In order to counter the data sparsity problem of the NMT training, MT researchers have proposed various strategies, e.g. augmenting training data, exploiting training data from other languages, alternative learning strategies that use only monolingual data. This paper presents a survey on recent advances of NMT research from the perspective of low-resource scenarios.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Machine Translation
ISSN
0922-6567
e-ISSN
1573-0573
Volume of the periodical
35
Issue of the periodical within the volume
4
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
24
Pages from-to
451-474
UT code for WoS article
000712946800001
EID of the result in the Scopus database
2-s2.0-85118298008