Data Selection for Unsupervised Translation of German--Upper Sorbian

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    This paper describes the methods behind the systems submitted by the University of Groningen for the WMT 2020 Unsupervised Machine Translation task for German--Upper Sorbian. We investigate the usefulness of data selection in the unsupervised setting. We find that we can perform data selection using a pretrained model and show that the quality of a set of sentences or documents can have a great impact on the performance of the UNMT system trained on it. Furthermore, we show that document-level data selection should be preferred for training the XLM model when possible. Finally, we show that there is a trade-off between quality and quantity of the data used to train UNMT systems.
    Original languageEnglish
    Title of host publicationProceedings of the Fifth Conference on Machine Translation (WMT)
    PublisherAssociation for Computational Linguistics, ACL Anthology
    Number of pages5
    Publication statusPublished - Nov-2020
    EventFifth Conference on Machine Translation - Online
    Duration: 19-Nov-202020-Nov-2020


    ConferenceFifth Conference on Machine Translation
    Abbreviated titleWMT20


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