The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation

Chris van der Lee*, Chris Emmery, Sander Wubben, Emiel Krahmer

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    Abstract

    This paper describes the CACAPO dataset, built for training both neural pipeline and end-to-end data-to-text language generation systems. The dataset is multilingual (Dutch and English), and contains almost 10,000 sentences from human-written news texts in the sports, weather, stocks, and incidents domain, together with aligned attribute-value paired data. The dataset is unique in that the linguistic variation and indirect ways of expressing data in these texts reflect the challenges of real world NLG tasks.
    Original languageEnglish
    Title of host publicationProceedings of The 13th International Conference on Natural Language Generation
    Place of PublicationDublin, Ireland
    Pages68-79
    Number of pages15
    Publication statusPublished - 1 Dec 2020
    EventInternational Conference on Natural Language Generation - online, Dublin , Ireland
    Duration: 15 Dec 202018 Dec 2020
    Conference number: 13
    https://www.inlg2020.org/

    Conference

    ConferenceInternational Conference on Natural Language Generation
    Abbreviated titleINLG 2020
    Country/TerritoryIreland
    CityDublin
    Period15/12/2018/12/20
    Internet address

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