Underspecified Universal Dependency Structures as Inputs for Multilingual Surface Realisation

Simon Mille, Anja Belz, Bernd Bohnet, Leo Wanner

    Research output: Chapter in Book/Conference proceeding with ISSN or ISBNConference contribution with ISSN or ISBNpeer-review


    In this paper, we present the datasets used in the Shallow and Deep Tracks of the First Multilingual Surface Realisation Shared Task (SR’18).
    For the Shallow Track, data in ten languages has been re- leased: Arabic, Czech, Dutch, English, Finnish, French, Italian, Portuguese, Russian and Spanish. For the Deep Track, data in three languages is made available: English, French and Spanish. We describe in detail how the datasets were derived from
    the Universal Dependencies V2.0, and report on an evaluation of the Deep Track input quality. In addition, we examine the motivation for, and likely usefulness of, deriving NLG inputs from annotations in resources originally developed for Natural Language Understanding (NLU), and
    assess whether the resulting inputs supply enough information of the right kind for the final stage in the NLG process.
    Original languageEnglish
    Title of host publicationProceedings of the 11th International Natural Language Generation Conference
    PublisherThe Association for Computational Linguistics
    Publication statusPublished - 1 Nov 2018
    Event11th International Conference on Natural Language Generation - Tilburg University, Tilburg, Netherlands
    Duration: 5 Nov 20188 Nov 2018


    Conference11th International Conference on Natural Language Generation
    Abbreviated titleINLG2018
    Internet address

    Bibliographical note

    © The author(s) | ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in {Source Publication}, http://dx.doi.org/10.1145/{number}


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