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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    In recent years, neural network-based machine translation (MT) approaches have steadily superseded the statistical MT (SMT) methods, and represents the current state-of-the-art in MT research.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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

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