Document context neural machine translation with memory networks

Sameen Maruf, Gholamreza Haffari

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    111 Citations (Scopus)


    We present a document-level neural machine translation model which takes both
    source and target document context into account using memory networks. We
    model the problem as a structured prediction problem with interdependencies
    among the observed and hidden variables, i.e., the source sentences and their unobserved target translations in the document. The resulting structured prediction problem is tackled with a neural translation model equipped with two memory components, one each for the source and target side, to capture the documental interdependencies. We train the model end-to-end, and propose an iterative decoding algorithm based on block coordinate descent. Experimental results of English translations from French, German, and Estonian documents show that our model is effective in exploiting both source and target document context, and statistically significantly outperforms the previous work in terms of BLEU and METEOR.
    Original languageEnglish
    Title of host publicationACL 2018 - The 56th Annual Meeting of the Association for Computational Linguistics
    Subtitle of host publicationProceedings of the Conference, Vol. 1 (Long Papers)
    EditorsIryna Gurevych, Yusuke Miyao
    Place of PublicationStroudsburg PA USA
    PublisherAssociation for Computational Linguistics (ACL)
    Number of pages10
    ISBN (Print)9781948087322
    Publication statusPublished - 2018
    EventAnnual Meeting of the Association of Computational Linguistics 2018 - Melbourne, Australia
    Duration: 15 Jul 201820 Jul 2018
    Conference number: 56th


    ConferenceAnnual Meeting of the Association of Computational Linguistics 2018
    Abbreviated titleACL 2018
    Internet address

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