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A Smaller and Better Word Embedding for 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%2F23%3ASGHGDUL7" target="_blank" >RIV/00216208:11320/23:SGHGDUL7 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.webofscience.com/wos/woscc/summary/e0b8ef34-8e6b-412a-9b8f-87607433ed44-bb92f483/relevance/1" target="_blank" >https://www.webofscience.com/wos/woscc/summary/e0b8ef34-8e6b-412a-9b8f-87607433ed44-bb92f483/relevance/1</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/access.2023.3270171" target="_blank" >10.1109/access.2023.3270171</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    A Smaller and Better Word Embedding for Neural Machine Translation

  • Original language description

    "Word embeddings play an important role in Neural Machine Translation (NMT). However, it still has a series of problems such as ignoring the prior knowledge of the association between words, relying on specific task constraints passively in parameter training, and isolating individual embedding's learning process from one another. In this paper, we propose a new word embedding method to add the prior knowledge of the association between words to the training process, and at the same time to share the iterative training results among all word embeddings. This method is applicable to all mainstream NMT systems. In our experiments, it achieves an improvement of +0.9 BLEU points on the WMT'14 English?German task. On the Global Voices v2018q4 Spanish?Czech low-resource translation tasks, it leads to a more prominent performance improvement over the strong baselines (a +2.6 BLEU improvement on average). As another "bonus", the new word embedding has far fewer parameters than the traditional word embedding, even as low as 15% of the parameters of the baselines."

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>ost</sub> - Miscellaneous article in a specialist periodical

  • 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

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    "IEEE ACCESS"

  • ISSN

    2169-3536

  • e-ISSN

  • Volume of the periodical

    11

  • Issue of the periodical within the volume

    2023

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    9

  • Pages from-to

    40770-40778

  • UT code for WoS article

    001033140800001

  • EID of the result in the Scopus database