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The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10424469" target="_blank" >RIV/00216208:11320/20:10424469 - isvavai.cz</a>

  • Result on the web

    <a href="http://www.statmt.org/wmt20/bib/2020.wmt-1.131.pdf" target="_blank" >http://www.statmt.org/wmt20/bib/2020.wmt-1.131.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    The LMU Munich System for the WMT20 Very Low Resource Supervised MT Task

  • Original language description

    We present our systems for the WMT20 Very Low Resource MT Task for translation between German and Upper Sorbian. For training our systems, we generate synthetic data by both back- and forward-translation. Additionally, we enrich the training data with German-Czech translated from Czech to Upper Sorbian by an unsupervised statistical MT system incorporating orthographically similar word pairs and transliterations of OOV words. Our best translation system between German and Sorbian is based on transfer learning from a Czech-German system and scores 12 to 13 BLEU higher than a baseline system built using the available parallel data only.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • 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

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Fifth Conference on Machine Translation - Proceedings of the Conference

  • ISBN

    978-1-948087-81-0

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    1102-1109

  • Publisher name

    Association for Computational Linguistics

  • Place of publication

    Stroudsburg, PA, USA

  • Event location

    Online

  • Event date

    Nov 19, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article